Not a complaint, but both the final images have poor contrast, lighting, saturation and colour balance, making them a disappointing target for an explanation of how these elements are produced from raw sensor data.But anyway, I enjoyed the article.

I&#x27;m not sure I understand your complaint. The &quot;expected result&quot; is either of the last two images (depending on your preference), and one of the main points of the post is to challenge the notion of &quot;ground truth&quot; in the first place.

Very interesting, pity the author chose such a poor example for the explanation (low, artificial and multicoloured light), making it really hard to understand what the &quot;ground truth&quot; and expected result should be.

Correction is useful for a bunch of different reasons, not all of them related to monitors. Even ISP pipelines without displays involved will still usually do it to allocate more bits to the highlights&#x2F;shadows than the relatively distinguishable middle bits. Old CRTs did it because the electron gun had a non-linear response and the gamma curve actually linearized the output. Film processing and logarithmic CMOS sensors do it because the sensing medium has a nonlinear sensitivity to the light level.

Which is why I&#x27;m looking at replacing my car&#x27;s rear-view mirror with a camera and a monitor. Because I can hard-cap the monitor brightness and curve the brightness below that, eliminating the problem of billion-lumens headlights behind me.

That&#x27;s a matter of tone mapping which is separate from gamma encoding? Even today, linearized pixel value 255 will be displayed at your defined SDR brightness no matter what. Changing your encoding gamma won&#x27;t help that because for correct output the transform necessarily needs to be be undone during display.

Linear encoding doesn&#x27;t change the max brightness of the monitor.More importantly, the camera isn&#x27;t recording blinding brightness in the first place! It&#x27;ll say those pixels are pure white, which is probably a few hundred or thousand nits depending on shutter settings.

If we&#x27;re talking about a sunset, then we&#x27;re talking about your monitor shooting out blinding, eye-hurting brightness light wherever the sun is in the image. That wouldn&#x27;t be very pleasant.

&gt;Human light intensity perception is not linear... You have to nonlinearize at some point of the pipelineWhy exactly? My understanding is that gamma correction is effectively a optimization scheme during encoding to allocate bits in a perceptually uniform way across the dynamic range. But if you just have enough bits to work with and are not concerned with file sizes (and assuming all hardware could support these higher bit depths), then this shouldn&#x27;t matter? IIRC unlike crts, LCDs don&#x27;t have a power curve response in terms of the hardware anyway, and emulate the overall 2.2 trc via LUT. So you could certainly get monitors to accept linear input (assuming you manage to crank up the bit depth enough to the point where you&#x27;re not losing perceptual fidelity), and just do everything in linear light.In fact if you just encoded the linear values as floats that would probably give you best of both worlds, since floating point is basically log-encoding where density of floats is lower at the higher end of the range.<a href="https:&#x2F;&#x2F;www.scantips.com&#x2F;lights&#x2F;gamma2.html" rel="nofollow">https:&#x2F;&#x2F;www.scantips.com&#x2F;lights&#x2F;gamma2.html</a> (I don&#x27;t agree with a lot of the claims there, but it has a nice calculator)

The shape of the curve doesn&#x27;t matter at all. What matters is having a mismatch between the capture curve and the display curve.If you kept it linear all the way to the output pixels, it would look fine. You only have to go nonlinear because the screen expects nonlinear data. The screen expects this because it saves a few bits, which is nice but far from necessary.To put it another way, it appears so dark because it isn&#x27;t being &quot;displayed directly&quot;. It&#x27;s going directly out to the monitor, and the chip inside the monitor is distorting it.

No. It&#x27;s about the shape of the curve. Human light intensity perception is not linear. You have to nonlinearize at some point of the pipeline, but yes, typically you should use high-resolution (&gt;=16 bits per channel) linear color in calculations and apply the gamma curve just before display. The fact that traditionally this was not done, and linear operations like blending were applied to nonlinear RGB values, resulted in ugly dark, muddy bands of intermediate colors even in high-end applications like Photoshop.

The problem with their example is that you can display linear image data just fine, just not with JPEG. Mapping linear data to 255 RGB that expects the gamma-corrected values is just wrong. They could have used an image format that supports linear data, like JPEG-XL, AVIF or HEIC. No conversion to 0-255 required, just throw in the data as-is.

&gt;if the linear data is displayed directly, it will appear much darker then it should be.This seems more a limitation of monitors. If you had very large bit depth, couldn&#x27;t you just display images in linear light without gamma correction.

&gt; A better discriminator might be global edits vs local edits,Even that isn&#x27;t all that clear-cut. Is noise removal a local edit? It only touches some pixels, but obviously, that&#x27;s a silly take.Is automated dust removal still global? The same idea, just a bit more selective. If we let it slide, what about automated skin blemish removal? Depth map + relighting, de-hazing, or fake bokeh? I think that modern image processing techniques really blur the distinction here because many edits that would previously need to be done selectively by hand are now a &quot;global&quot; filter that&#x27;s a single keypress away.Intent is the defining factor, as you note, but intent is... often hazy. If you dial down the exposure to make the photo more dramatic &#x2F; more sinister, you&#x27;re manipulating emotions too. Yet, that kind of editing is perfectly OK in photojournalism. Adding or removing elements for dramatic effect? Not so much.

&gt; &quot;Fake&quot; images are images with intent to deceiveThe ones that make the annual rounds up here in New England are those foliage photos with saturation jacked. “Look at how amazing it was!” They’re easy to spot since doing that usually wildly blows out the blues in the photo unless you know enough to selectively pull those back.

There&#x27;s an obvious difference between debayering and white balance vs using Photoshop&#x27;s generative fill

I understand what you and the article are saying, but what GP is getting at, and what I agree with, is that there is a difference between a photo that attempts to reproduce what the &quot;average&quot; human sees, and digital processing that augments the image in ways that no human could possibly visualize. Sometimes we create &quot;fake&quot; images to improve clarity, detail, etc., but that&#x27;s still less &quot;fake&quot; than smoothing skin to remove blemishes, or removing background objects. One is clearly a closer approximation of how we perceive reality than the other.So there are levels of image processing, and it would be wrong to dump them all in the same category.

Everything is an interpretation of the data that the camera has to doWhat about this? <a href="https:&#x2F;&#x2F;news.ycombinator.com&#x2F;item?id=35107601">https:&#x2F;&#x2F;news.ycombinator.com&#x2F;item?id=35107601</a>

&gt; What most people think when they say &quot;fake&quot;, though, is a photo that has had filters applied, which makes zero sense. As the post shows, all photos have filters applied.Filters themselves don&#x27;t make it fake, just like words themselves don&#x27;t make something a lie. How the filters and words are used, whether they bring us closer or further from some truth, is what makes the difference.Photos implicitly convey, usually, &#x27;this is what you would see if you were there&#x27;. Obviously filters can help with that, as in the OP, or hurt.

Well that&#x27;s why back in the day (and even still) &#x27;Photographer listing their whole kit for every shot&#x27; is a thing thing you sometimes see.i.e. Camera+Lens+ISO+SS+FStop+FL+TC (If present)+Filter (If present). Add focus distance if being super duper proper.And some of that is to help at least provide the right requirements to try to recreate.

Today, I trust the other meaning of &quot;fake images&quot; is that an image was generated by AI.

But when you shift the goal posts that far, a real image has never been produced. But people very clearly want to describe when an image has been modified to represent something that didn’t happen.

Eh, I&#x27;m a photographer and I don&#x27;t fully agree. Of course almost all photos these days are edited in some form. Intent is important, yes. But there are still some kinds of edits that immediately classify a photo as &quot;fake&quot; for me.For example if you add snow to a shot with masking or generative AI. It&#x27;s fake because the real life experience was not actually snowing. You can&#x27;t just hallucinate a major part of the image - that counts as fake to me. A major departure from the reality of the scene. Many other types of edits don&#x27;t have this property because they are mostly based on the reality of what occurred.I think for me this comes from an intrinsic valuing of the act&#x2F;craft of photography, in the physical sense. Once an image is too digitally manipulated then it&#x27;s less photography and more digital art.

A boss once asked me &quot;is there a way to tell if an image has been Photoshopped?&quot; and I did eventually get him to &quot;yes, if you can see the image it has been digitally processed and altered by that processing&quot;. (The brand-name-as-generic conversation was saved for another day.)

Another low tech example - those telephoto crowd shots that were popular during covid. The &#x27;deception&#x27; happens before the light hits the sensor, but it&#x27;s no less effective<a href="https:&#x2F;&#x2F;www.theguardian.com&#x2F;australia-news&#x2F;2020&#x2F;sep&#x2F;13&#x2F;picture-imperfect-why-photos-of-crowded-beaches-may-not-be-what-they-seem" rel="nofollow">https:&#x2F;&#x2F;www.theguardian.com&#x2F;australia-news&#x2F;2020&#x2F;sep&#x2F;13&#x2F;pictu...</a>

I agree global vs local edits as a discriminator, but there is a bit of a sliding scale here. For instance when you edit a photo of something that is lit by multiple light sources that have different color temperatures. The photo will produce a representation that shows more dramatic differences than you might be aware of when you look at the same scene. So when editing a photo, you may apply some processing to different areas to nudge the colors closer to how you’d “see” them.Ditto for black and white photos. Your visual perception has pretty high dynamic range. Not least because your eyes move and your brain creates a representation that gives you the illusion of higher dynamic range than what your eyes can actually deliver. So when you want to represent it using a technology that can only give you a fraction of the dynamic range you (or your camera) can see, you sometimes make local’ish edits (eg create a mask with brush or gradients to lighten or darken regions)Ansel Adams did a lot of dodging and burning in his prints. Some of the more famous ones are very obvious in terms of having been “processed” during the exposure of the print.I see this as overcoming the limitations in conveying what your eyes&#x2F;brain will see when using the limited capabilities of camera&#x2F;screen&#x2F;print. It is local’ish edits, but the intent isn’t so much to deceive as it is to nudge information into a range where it can be seen&#x2F;understood.

Sidestepping the whole discussion about &quot;fake&quot; and &quot;real&quot; images, I think what matters is what degree of correlation is there, if any, between the raw sensor data and the final photo you show your friends.Raw data requires interpretation, no argument there.But when AI starts making stuff up out of nowhere, it becomes a problem. Again, some degree of making up stuff is ok, but AI often crosses the line. When it diverges enough from what was captured by the sensor, it crosses firmly into &quot;made up&quot; territory.

One thing I&#x27;ve learned while dabbling in photography is that there are no &quot;fake&quot; images, because there are no &quot;real&quot; images. Everything is an interpretation of the data that the camera has to do, making a thousand choices along the way, as this post beautifully demonstrates.A better discriminator might be global edits vs local edits, with local edits being things like retouching specific parts of the image to make desired changes, and one could argue that local edits are &quot;more fake&quot; than global edits, but it still depends on a thousand factors, most importantly intent.&quot;Fake&quot; images are images with intent to deceive. By that definition, even an image that came straight out of the camera can be &quot;fake&quot; if it&#x27;s showing something other than what it&#x27;s purported to (e.g. a real photo of police violence but with a label saying it&#x27;s in a different country is a fake photo).What most people think when they say &quot;fake&quot;, though, is a photo that has had filters applied, which makes zero sense. As the post shows, all photos have filters applied. We should get over that specific editing process, it&#x27;s no more fake than anything else.

ML demosaicing algorithms (e.g. convolutional neural networks) are the state of the art for reversing camera color filters, and this was true back when I did my post-grad studies on the subject almost 10 years ago, not to mention all the other stages of the post-processing stack. So one will have to wrestle with the fact that some form of &quot;AI&quot; has been part of digital images for a while now.I mean to some degree, human perception is a hallucination of reality. It is well known by magicians that if you know the small region of space that a person is focusing on, then you can totally change other areas of the scene without the person noticing.

For me personally, it&#x27;s fine to do things like local tone mapping, but object segmentation is where I draw the line. As in, a camera shouldn&#x27;t know what a sky or a tree or a person is. It shouldn&#x27;t care about any of that. It shouldn&#x27;t process different parts of the image differently depending on what it &quot;thinks&quot; is there. Also, denoising should be configurable, because I would always prefer noise over this stupid &quot;painted&quot; look.

I think everyone agrees that dynamic range compression and de-Bayering (for sensors which are colour-filtered) are necessary for digital photography, but at the other end of the spectrum is &quot;use AI to recognise objects and hallucinate what they &#x27;should&#x27; look like&quot; --- and despite how everyone would probably say that isn&#x27;t a real photo anymore, it seems manufacturers are pushing strongly in that direction, raising issues with things like admissibility of evidence.

I don&#x27;t think Kindle ads were available in my region in 2015 because I don&#x27;t remember seeing these back then, but you&#x27;re a lucky one to fix this classic mistake :-)I remember trying out some of the home-made methods while I was implementing a creative work section for a school assignment. It’s surprising how &quot;flat&quot; the basic average looks until you actually respect the coefficients (usually some flavor of 0.21R + 0.72G + 0.07B). I bet it&#x27;s even more apparent in a 4-bit display.

I wouldn&#x27;t worry about it too much, looking at ads is always a shitty experience. Correctly grayscaled or not.

I have a related anecdote.When I worked at Amazon on the Kindle Special Offers team (ads on your eink Kindle while it was sleeping), the first implementation of auto-generated ads was by someone who didn&#x27;t know that properly converting RGB to grayscale was a smidge more complicated than just averaging the RGB channels. So for ~6 months in 2015ish, you may have seen a bunch of ads that looked pretty rough. I think I just needed to add a flag to the FFmpeg call to get it to convert RGB to luminance before mapping it to the 4-bit grayscale needed.

JPEG with OOC processing is different from JPEG OOPC (out-of-phone-camera) processing. Thank Samsung for forcing the need to differentiate.

it&#x27;s not a crime but applying post processing in an overly generous way that goes a lot further than replicating what a human sees does take away from what makes pictures interesting imho vs other mediums, that it&#x27;s a genuine representation of something that actually happened.if you take that away, a picture is not very interesting, it&#x27;s hyperrealistic so not super creative a lot of the time (compared to eg paintings), &amp; it doesn&#x27;t even require the mastery of other mediums to get hyperrealistism

There&#x27;s a difference between an unbiased (roughly speaking) pipeline and what (for example) JBIG2 did. The latter counts as &quot;editing&quot; and &quot;fake&quot; as far as I&#x27;m concerned. It may not be a crime but at least personally I think it&#x27;s inherently dishonest to attempt to play such things off as &quot;original&quot;.And then there&#x27;s all the nonsense BigTech enables out of the box today with automated AI touch ups. That definitely qualifies as fakery although the end result may be visually pleasing and some people might find it desirable.

As a mostly amateur photographer, it doesn&#x27;t bother me if people ask that question.  While I understand the point that the camera itself may be making some &#x27;editing&#x27; type decision on the data first, a) in theory each camera maker has attempted to calibrate the output to some standard, b) public would expect two photos taken at same time with same model camera should look identical. That differs greatly from what often can happen in &quot;post production&quot; editing - you&#x27;ll never find two that are identical.

I noticed this a lot when taking pictures in the mountains.I used to have a high resolution phone camera from a cheaper phone and then later switched to an iPhone. The latter produced much nicer pictures, my old phone just produces very flat-looking pictures.People say that the iPhone camera automatically edits the images to look better. And in a way I notice that too. But that’s the wrong way of looking at it; the more-edited picture from the iPhone actually corrresponds more to my perception when I’m actually looking at the scene. The white of the snow and glaciers and the deep blue sky really does look amazing in real life, and when my old phone captured it into a flat and disappointing looking photo with less postprocessing than an iPhone, it genuinely failed to capture what I can see with my eyes. And the more vibrant post processed colours of an iPhone really do look more like what I think I’m looking at.

I don&#x27;t think it&#x27;s the same, for me personally I don&#x27;t like heavily processed images. But not in the sense that they need processing to look decent or to convey the perception of what it was like in real life, more in the sense that the edits change the reality in a significant way so it affects the mood and the experience. For example, you take a photo on a drab cloudy day, but then edit the white balance to make it seem like golden hour, or brighten a part to make it seems like a ray of light was hitting that spot. Adjusting the exposure, touching up slightly, that&#x27;s all fine, depending on what you are trying to achieve of course. But what I see on instagram or shorts these days is people comparing their raws and edited photos, and without the edits the composition and subject would be just mediocre and uninteresting.

That&#x27;s completely unreasonable. Sure the camera processes them heavily. but when you open it up and start editing in photoshop you are changing this area over that one, or highlighting one color over another etc.. or just boosting the brightness way higher than what it looked like that day. It&#x27;s a perfectly normal question to ask.

This is also why I absolute hate, hate, hate it when people ask me whether I &quot;edited&quot; a photo or whether a photo is &quot;original&quot;, as if trying to explain away nice-looking images as if they are fake.The JPEGs cameras produce are heavily processed, and they are emphatically NOT &quot;original&quot;. Taking manual control of that process to produce an alternative JPEG with different curves, mappings, calibrations, is not a crime.

found the guy who didn&#x27;t know about em dashes before this yearalso your question implies a bad assumption even if you disclaim it. if you don&#x27;t want to imply a bad assumption the way to do that is to not say the words, not disclaim them

Upon inspection, the author&#x27;s personal website used em dashes in 2023. I hope this helped with your witch hunt.I&#x27;m imagining a sort of Logan&#x27;s Run-like scifi setup where only people with a documented em dash before November 30, 2022, i.e. D(ash)-day, are left with permission to write.

I have been overusing em dashes and bulleted lists since the actual 80s, I&#x27;m sad to say. I spent much of the 90s manually typing &quot;smart&quot; quotes.I have actually been deliberately modifying my long-time writing style and use of punctuation to look less like an LLM. I&#x27;m not sure how I feel about this.

hey, not accusing you of anything (bad assumptions don&#x27;t lead to a conducive conversation) but did you use AI to write or assist with this comment?this is totally out of my own self-interest, no problems with its content

Several reasons, 
-Silicon efficiency (QE) peaks in the green
-Green spectral response curve is close to the luminance curve humans see, like you said.
-Twice the pixels to increase the effective resolution in the green&#x2F;luminance channel, color channels in YUV contribute almost no details.Why is YUV or other luminance-chrominance color spaces important for a RGB input? Because many processing steps and encoders, work in YUV colorspaces. This wasn&#x27;t really covered in the article.

Green is in the center of the visible spectrum of light (notice the G in the middle of ROYGBIV), so evolution should theoretically optimize for green light absorption. An interesting article on why plants typically reflect that wavelength and absorb the others: <a href="https:&#x2F;&#x2F;en.wikipedia.org&#x2F;wiki&#x2F;Purple_Earth_hypothesis" rel="nofollow">https:&#x2F;&#x2F;en.wikipedia.org&#x2F;wiki&#x2F;Purple_Earth_hypothesis</a>

Not sure why it would invoke such strong sentiments but if you don’t like the bayer filter, know that some true monochrome cameras don’t use it and make every sensor pixel available to the final image.For instance, the Leica M series have specific monochrome versions with huge resolutions and better monochrome rendering.You can also modify some cameras and remove the filter, but the results usually need processing.
A side effect is that the now exposed sensor is more sensitive to both ends of the spectrum.

If the Bayer pattern makes you angry, I imagine it would really piss you off to realize that the whole concept encoding an experienced color by a finite number of component colors is fundamentally species-specific and tied to the details of our specific color sensors.To truly record an appearance without reference to the sensory system of our species, you would need to encode the full electromagnetic spectrum from each point. Even then, you would still need to decide on a cutoff for the spectrum....and hope that nobody ever told you about coherence phenomena.

You think that&#x27;s bad? Imagine finding out that all video still encodes colour at half resolution simply because that is how analog tv worked.

the bayer pattern is one of those things that makes me irrationally angry, in the true sense, based on my ignorance of the subjectwhat&#x27;s so special about green? oh so just because our eyes are more sensitive to green we should dedicate double the area to green in camera sensors? i mean, probably yes. but still. (⩺_⩹)

Exactly - film photographers heavily process(ed) their images from the film processing through to the print. Ansel Adams wrote a few books on the topic and they’re great reads.And different films and photo papers can have totally different looks, defined by the chemistry of the manufacturer and however _they_ want things to look.

True, but there may be different intentions behind the processing.Sometimes the processing has only the goal to compensate the defects of the image sensor and of the optical elements, in order to obtain the most accurate information about the light originally coming from the scene.Other times the goal of the processing is just to obtain an image that appears best to the photographer, for some reason.For casual photographers, the latter goal is typical, but in scientific or technical applications the former goal is frequently encountered.Ideally, a &quot;raw&quot; image format is one where the differences between it and the original image are well characterized and there are no additional unknown image changes done for an &quot;artistic&quot; effect, in order to allow further processing when having either one of the previously enumerated goals.

Yes. Writing a post like this, but for film, would be illustrative of that similarity, but significantly more challenging to represent, especially for color film. I actually don&#x27;t know the whole process in enough detail to write one, and the visualizations would be difficult, but the processing is there.You have layers of substrate with silver halides, made sensitive to different frequency ranges with sensitizing dyes, crystallized into silver halide crystals, rather than a regular grid of pixels; you take a photo that is not an image, but a collection of specks of metallic silver. Through a series of chemical reactions, you develop those specks. Differences in chemistry, in temperatures, in agitation, in the film, all affect what for digital images is described as processing. Then in printing, you have a similar process all over again.If anything, one might argue that the digital process allows a more consistent and quantitative understanding of the actual processing being done. Analog film seems like it involves less processing only because, for most people, the processing was always a black box of sending off the film for development and printing.

Even old school chemical films were the same thing, just different domain.There is no such thing as “unprocessed” data, at least that we can perceive.

Funnily enough that&#x27;s not the only mistake he made in that article. His final image is noticeably different from the camera&#x27;s output image because he rescaled the values in the first step. That&#x27;s why the dark areas look so crushed, eg around the firewood carrier on the lower left or around the cat, and similarly with highlights, e.g. the specular highlights on the ornaments.After that, the next most important problem is the fact he operates in the wrong color space, where he&#x27;s boosting raw RGB channels rather than luminance. That means that some objects appear much too saturated.So his photo isn&#x27;t &quot;unprocessed&quot;, it&#x27;s just incorrectly processed.

If someone&#x27;s curious about those particular constants, they&#x27;re the PAL Y&#x27; matrix coefficients: <a href="https:&#x2F;&#x2F;en.wikipedia.org&#x2F;wiki&#x2F;Y%E2%80%B2UV#SDTV_with_BT.470" rel="nofollow">https:&#x2F;&#x2F;en.wikipedia.org&#x2F;wiki&#x2F;Y%E2%80%B2UV#SDTV_with_BT.470</a>

&gt; The quantization formula ppmtopgm uses is g = .299 r + .587 g + .114 b.Seriously. We can trust linux man pages to use the same 1-letter variable name for 2 different things in a tiny formula, can&#x27;t we?

&gt; A fun tangent on the &quot;green cast&quot; mentioned in the post: the reason the Bayer pattern is RGGB (50% green) isn&#x27;t just about color balance, but spatial resolution. The human eye is most sensitive to green light, so that channel effectively carries the majority of the luminance (brightness&#x2F;detail) data.From the classic file format &quot;ppm&quot; (portable pixel map) the ppm to pgm (portable grayscale map) man page:<a href="https:&#x2F;&#x2F;linux.die.net&#x2F;man&#x2F;1&#x2F;ppmtopgm" rel="nofollow">https:&#x2F;&#x2F;linux.die.net&#x2F;man&#x2F;1&#x2F;ppmtopgm</a><pre><code> The quantization formula ppmtopgm uses is g = .299 r + .587 g + .114 b.
</code></pre>
You&#x27;ll note the relatively high value of green there, making up nearly 60% of the luminosity of the resulting grayscale image.I also love the quote in there...<pre><code> Quote

 Cold-hearted orb that rules the night
 Removes the colors from our sight
 Red is gray, and yellow white
 But we decide which is right
 And which is a quantization error.
</code></pre>
(context for the original - <a href="https:&#x2F;&#x2F;www.youtube.com&#x2F;watch?v=VNC54BKv3mc" rel="nofollow">https:&#x2F;&#x2F;www.youtube.com&#x2F;watch?v=VNC54BKv3mc</a> )

This ratio allows for a relatively simple 2x2 repeating pattern. That makes interpolating the values immensely simpler.Also you don&#x27;t want the red and blue to be too far apart, reconstructing the colour signal is difficult enough as it is. Moire effects are only going to get worse if you use an even sparser resolution.

Why that ratio in particular? I wonder if there’s a more complex ratio that could be better.

Thanks! First hit online: <a href="https:&#x2F;&#x2F;www.lab404.com&#x2F;3741&#x2F;readings&#x2F;sontag.pdf" rel="nofollow">https:&#x2F;&#x2F;www.lab404.com&#x2F;3741&#x2F;readings&#x2F;sontag.pdf</a>Out of curiosity: what led you to write &quot;Susan Sunday&quot; instead of &quot;Susan Sontag&quot;? (for other readers: &quot;Sonntag&quot; is German for &quot;Sunday&quot;)

&gt; modern photography is just signal processing with better marketingI pass on a gift I learned of from HN: Susan Sunday’s “On Photography”.

The cones are the colour sensitive portion of the retina, but only make up a small percent of all the light detecting cells. The rods (more or less the brightness detecting cells) would still function in a deuteranopic person, so their luminance perception would basically be unaffected.Also there’s something to be said about the fact that the eye is a squishy analog device, and so even if the medium wavelengths cones are deficient, long wavelength cones (red-ish) have overlap in their light sensitivities along with medium cones so…

It’s not that their M-cones (middle, i.e. green) don’t work at all, their M-cones responsivity curve is just shifted to be less distinguishable from their L-cones curve, so they effectively have double (or more) the “red sensors”.

Protanopia and protanomaly shift luminance perception away from the longest wavelengths of visible light, which causes highly-saturated red colours to appear dark or black. Deuteranopia and deuteranomaly don&#x27;t have this effect. [1]Blue cones make little or no contribution to luminance. Red cones are sensitive across the full spectrum of visual light, but green cones have no sensitivity to the longest wavelengths [2]. Since protans don&#x27;t have the &quot;hardware&quot; to sense long wavelengths, it&#x27;s inevitable that they&#x27;d have unusual luminance perception.I&#x27;m not sure why deutans have such a normal luminous efficiency curve (and I can&#x27;t find anything in a quick literature search), but it must involve the blue cones, because there&#x27;s no way to produce that curve from the red-cone response alone.[1]: <a href="https:&#x2F;&#x2F;en.wikipedia.org&#x2F;wiki&#x2F;Luminous_efficiency_function#Color_blindness" rel="nofollow">https:&#x2F;&#x2F;en.wikipedia.org&#x2F;wiki&#x2F;Luminous_efficiency_function#C...</a>[2]: <a href="https:&#x2F;&#x2F;commons.wikimedia.org&#x2F;wiki&#x2F;File:Cone-fundamentals-with-srgb-spectrum.svg" rel="nofollow">https:&#x2F;&#x2F;commons.wikimedia.org&#x2F;wiki&#x2F;File:Cone-fundamentals-wi...</a>

&gt; The human eye is most sensitive to green light, so that channel effectively carries the majority of the luminance (brightness&#x2F;detail) dataHow does this affect luminance perception for deuteranopes? (Since their color blindness is caused by a deficiency of the cones that detect green wavelengths)

And this is just what happens for a single frame. It doesn&#x27;t even touch computational photography[1].[1] <a href="https:&#x2F;&#x2F;dpreview.com&#x2F;articles&#x2F;9828658229&#x2F;computational-photography-part-i-what-is-computational-photography" rel="nofollow">https:&#x2F;&#x2F;dpreview.com&#x2F;articles&#x2F;9828658229&#x2F;computational-photo...</a>

Note that there are two measurement systems involved: first the camera, and then the human eyes. Your reasoning could be correct if there were only one: &quot;the sensor is most sensitive to green light, so less sensor area is needed&quot;.But it is not the case, we are first measuring with cameras, and then presenting the image to human eyes. Being more sensitive to a colour means that the same measurement error will lead to more observable artifacts. So to maximize visual authenticity, the best we can do is to make our cameras as sensitive to green light (relatively) as human eyes.

Since the human eye is most sensitive to green, it will find errors in the green channel much easier than the others. This is why you need _more_ green data.

Yeah, was thinking the same. If we&#x27;re more sensitive, why do we need double sensors? Just have 1:1:1, and we would anyways see more of the green? Won&#x27;t it be too much if we do 1:2:1, when we&#x27;re already more perceptible to green?

Because that reasoning applies to binary signals, where the sensibility is about detection, in the case of our eyes sensibility means that we can detect many more distinct values let&#x27;s say we can see N distinct luminosity levels of monochrome green light but only N*k or N^k distinct levels of blue light.So to describe&#x2F;reproduce what our eyes see you need more detection range in the green spectrum

&gt; The human eye is most sensitive to green light,This argument is very confusing: if is most sensitive, less intensity&#x2F;area should be necessary, not more.

&gt; we are most sensitive to red light&gt; green does indeed overwhelmingly contribute to perceptual luminanceso... if luminance contribution is different from &quot;sensitivity&quot; to you - what do you imply by sensitivity?

Is it related to the fact that monkeys&#x2F;humans evolved around dense green forests ?

&gt; Small neural networks I believe are the current state of the art (e.g. train to reverse a 16x16 color filter pattern for the given camera). What is currently in use by modern digital cameras is all trade secret stuff.Considering you usually shoot RAW, and debayer and process in post, the camera hasn&#x27;t done any of that.It&#x27;s only smartphones that might be doing internal AI Debayering, but they&#x27;re already hallucinating most of the image anyway.

The bit about the green over-representation in camera color filters is partially correct. Human color sensitivity varies a lot from individual to individual (and not just amongst individuals with color blindness), but general statistics indicate we are most sensitive to red light.The main reason is that green does indeed overwhelmingly contribute to perceptual luminance (over 70% in sRGB once gamma corrected: <a href="https:&#x2F;&#x2F;www.w3.org&#x2F;TR&#x2F;WCAG20&#x2F;#relativeluminancedef" rel="nofollow">https:&#x2F;&#x2F;www.w3.org&#x2F;TR&#x2F;WCAG20&#x2F;#relativeluminancedef</a>) and modern demosaicking algorithms will rely on both derived luminance and chroma information to get a good result (and increasingly spatial information, e.g. &quot;is this region of the image a vertical edge&quot;).Small neural networks I believe are the current state of the art (e.g. train to reverse a 16x16 color filter pattern for the given camera). What is currently in use by modern digital cameras is all trade secret stuff.

Also worth noting that manufacturers advertise photodiode count as a sensor resolution. So if you have 12 Mp sensor then your green resolution is 6 Mp and blue and red are 3 Mp

Another tangent. Bryce Bayer is the dad of a HN poster. <a href="https:&#x2F;&#x2F;news.ycombinator.com&#x2F;item?id=12111995">https:&#x2F;&#x2F;news.ycombinator.com&#x2F;item?id=12111995</a> <a href="https:&#x2F;&#x2F;news.ycombinator.com&#x2F;item?id=36043826">https:&#x2F;&#x2F;news.ycombinator.com&#x2F;item?id=36043826</a>

&gt; It really highlights that modern photography is just signal processing with better marketing.Showing linear sensor data on a logarithmic output device to show how hard images are processed is an (often featured) sleight of hand, however.

&gt;It really highlights that modern photography is just signal processing with better marketingyour perception of the world is just signal processing that&#x27;s susceptible to marketing

I love posts that peel back the abstraction layer of &quot;images.&quot; It really highlights that modern photography is just signal processing with better marketing.A fun tangent on the &quot;green cast&quot; mentioned in the post: the reason the Bayer pattern is RGGB (50% green) isn&#x27;t just about color balance, but spatial resolution. The human eye is most sensitive to green light, so that channel effectively carries the majority of the luminance (brightness&#x2F;detail) data.
In many advanced demosaicing algorithms, the pipeline actually reconstructs the green channel first to get a high-resolution luminance map, and then interpolates the red&#x2F;blue signals—which act more like &quot;color difference&quot; layers—on top of it. We can get away with this because the human visual system is much more forgiving of low-resolution color data than it is of low-resolution brightness data. It’s the same psycho-visual principle that justifies 4:2:0 chroma subsampling in video compression.Also, for anyone interested in how deep the rabbit hole goes, looking at the source code for dcraw (or libraw) is a rite of passage. It’s impressive how many edge cases exist just to interpret the &quot;raw&quot; voltages from different sensor manufacturers.

My mirrorless camera shoots in RAW. When someone asks me if a certain photo was “edited”, I honestly don’t know what to answer. The files went through a RAW development suite that applied a bewildering amount of maths to transform them into a sRGB image. Some of the maths had sliders attached to it and I have moved some of the sliders, but their default positions were just what the software thought was appropriate. The camera isn’t even set to produce a JPEG + RAW combo, so there is literally no reference.

I don&#x27;t really think so!  I think they imagine that if there were no processing applied to the image it would look pretty much like what they see IRL but that&#x27;s not true.

They probably do know what they&#x27;re asking for, they&#x27;re just using an ambiguous word.

This is actually really useful.  A lot of people demand an &quot;unprocessed&quot; photo but don&#x27;t understand what they&#x27;re actually asking for.

So, for a 3x3 image, the input data would be 9 values like:<pre><code> R G B
 B R G
 G B R

?</code></pre>

It&#x27;s a single value per pixel, but each pixel has a different color filter in front of it, so it&#x27;s effectively that each pixel is one of R, G, or B

What about taking 3 photos while quickly changing the filter (e.g. filters are something like quantum dots that can be turned on&#x2F;off)?

B&amp;W sensors are generally more sensitive than their color versions, as all filters  (going back to signal processing..) attenuate the signal.

In its most raw form, camera sensors only see illumination not color.In front of the sensor is a bayer filter which results in each physical pixel seeing illumination filtered R G or B.From there the software onboard the camera or in your RAW converter does interpolation to create RGB values at each pixel. For example if the local pixel is R filtered, it then interpolates its G &amp; B values from nearby pixels of that filter.<a href="https:&#x2F;&#x2F;en.wikipedia.org&#x2F;wiki&#x2F;Bayer_filter" rel="nofollow">https:&#x2F;&#x2F;en.wikipedia.org&#x2F;wiki&#x2F;Bayer_filter</a>There are alternatives such as what Fuji does with its X-trans sensor filter.<a href="https:&#x2F;&#x2F;en.wikipedia.org&#x2F;wiki&#x2F;Fujifilm_X-Trans_sensor" rel="nofollow">https:&#x2F;&#x2F;en.wikipedia.org&#x2F;wiki&#x2F;Fujifilm_X-Trans_sensor</a>Another alternative is Foveon (owned by Sigma now) which makes full color pixel sensors but they have not kept up with state of the art.<a href="https:&#x2F;&#x2F;en.wikipedia.org&#x2F;wiki&#x2F;Foveon_X3_sensor" rel="nofollow">https:&#x2F;&#x2F;en.wikipedia.org&#x2F;wiki&#x2F;Foveon_X3_sensor</a>This is also why Leica B&amp;W sensor cameras have higher apparently sharpness &amp; ISO sensitivity than the related color sensor models because there is no filter in front or software interpolation happening.

The sensor outputs a single value per pixel. A later processing step is needed to interpret that data given knowledge about the color filter (usually Bayer pattern) in front of the sensor.

The raw sensor output is a single value per sensor pixel, each of which is behind a red, green, or blue color filter. So to get a usable image (where each pixel has a value for all three colors), we have to somehow condense the values from some number of these sensor pixels. This is the &quot;Debayering&quot; process.

I am confused by the color filter step.Is the output produced by the sensor RGB or a single value per pixel?

This is a great write up. It&#x27;s also weirdly similar to a video I happened upon yesterday playing around with raw Hubble imagery: <a href="https:&#x2F;&#x2F;www.youtube.com&#x2F;watch?v=1gBXSQCWdSI" rel="nofollow">https:&#x2F;&#x2F;www.youtube.com&#x2F;watch?v=1gBXSQCWdSI</a>He take a few minutes to get to the punch line. Feel free to skip ahead to around 5:30.

Wow this is amazing. What a good and simple explanation!

I said “flat” because I didn’t feel like going into “log” and color profiles and such but I’ll admit I’m leaning hard into over-simplification, because log, raw, etc. gets messy when discussing profiles vs codecs&#x2F;compression&#x2F;etc.  In video we still call some codecs “raw,” but it’s not the same necessarily as how it’s used in photography. Like the Red raw codec has various compression ratios (5:1 tends to be the sweet spot IME) and it really messes with the whole idea of what raw even is. It’s all quasi-technical and somewhat inconsistent.

&quot;Flat&quot; is a bit of a misnomer in this context. It&#x27;s not flat, it&#x27;s actually a logarithmic (&quot;log profile&quot;) representation of data computed by the camera to allow a wider dynamic range to be squeezed into traditional video formats.It&#x27;s sort of the opposite of what&#x27;s going on with photography, where you have a dedicated &quot;raw&quot; format with linear readings from the sensor. Without these formats, someone would probably have invented &quot;log JPEG&quot; or something like that to preserve more data in highlights and in the shadows.

For those who are curious, this is basically what we do when we color grade in video production but taken to its most extreme. Or rather, stripped down to the most fundamental level. Lots of ways to describe it.Generally we shoot “flat” (there are so many caveats to this but I don’t feel like getting bogged down in all of it. If you plan on getting down and dirty with colors and really grading, you generally shoot flat). The image that we handover to DIT&#x2F;editing can be borderline grayscale in its appearance. The colors are so muted, the dynamic range is so wide, that you basically have a highly muted image. The reason for this is you then have the freedom to “push” the color and look and almost any direction, versus if you have a very saturated, high contrast image, you are more “locked” into that look. This matters more and more when you are using a compressed codec and not something with an incredibly high bitrate or raw codecs, which is a whole other world and I am also doing a bit of a disservice to by oversimplifying.Though this being HN it is incredibly likely I am telling few to no people anything new here lol

There are also no citations, and it has this phrase &quot;This website is not licensed for ML&#x2F;LLM training or content creation.&quot; Yeah right, that&#x27;s like the privacy notice posts people make to facebook from time to time that contradict the terms of service <a href="https:&#x2F;&#x2F;knowyourmeme.com&#x2F;memes&#x2F;facebook-privacy-notices" rel="nofollow">https:&#x2F;&#x2F;knowyourmeme.com&#x2F;memes&#x2F;facebook-privacy-notices</a>

My point wasn&#x27;t &quot;I can&#x27;t find this information&quot;, my point was &quot;this is poorly written&quot;.

Right-click, &quot;Search Google for &#x27;ADC&#x27;&quot;, takes much less time than making this useless comment.<a href="https:&#x2F;&#x2F;en.wikipedia.org&#x2F;wiki&#x2F;Analog-to-digital_converter" rel="nofollow">https:&#x2F;&#x2F;en.wikipedia.org&#x2F;wiki&#x2F;Analog-to-digital_converter</a>

Likely analog to digital converter, digitizing the raw signal from the photodetector cells

The article keeps using the acronym &quot;ADC&quot; without defining it.

The reasons aren’t exactly unknown, considering that the sensor is diagonally oriented also?Processing these does seem like more fun though.

OK now do Fuji Super CCD (where for reasons unknown the RAW is diagonal [0])[0] - <a href="https:&#x2F;&#x2F;en.wikipedia.org&#x2F;wiki&#x2F;Super_CCD#&#x2F;media&#x2F;File:Fuji_CCD_DCRaw_2615.jpg" rel="nofollow">https:&#x2F;&#x2F;en.wikipedia.org&#x2F;wiki&#x2F;Super_CCD#&#x2F;media&#x2F;File:Fuji_CCD...</a>

I fully agree regarding gamma, but completely disagree when it comes to debayering. Unless you turn 2x2 Bayer blocks into a single RGB pixel (losing some data in the process), the point of debayering is to interpolate missing data - it&#x27;s upscaling of a kind after all - and you can use a multitude of various approaches to do that resulting in differing outputs.

Honestly, I think the gamma normalization step don&#x27;t really count as &quot;processing&quot;, any more than the gzip decompression step doesn&#x27;t count as &quot;processing&quot; for the purposes of &quot;this is what an unprocessed html file looks like&quot; demo. At the end of the day, it&#x27;s the same information, but encoded differently. Similar arguments can be made for de-bayer filter step. If you ignore these two steps, the &quot;processing&quot; that happens looks far less dramatic.

&gt; My favourite mental model revelation to come from it was that what I see isn’t the “ground truth.” It’s a view of a subset of the data. My eyes, my cat’s eyes, my cameras all collect and render different subsets of the data, providing different views of the subject matter.What a nice way to put it.

And not only that, our sensors can return spurious data, or even purposely constructed fake data, created with good or evil intent.I&#x27;ve had this in mind at times in recent years due to $DAYJOB. We use simulation heavily to provide fake CPUs, hardware devices, what have you, with the goal of keeping our target software happy by convincing it that it&#x27;s running in its native environment instead of on a developer laptop.Just keep in mind that it&#x27;s important not to go _too_ far down the rabbit hole, one can spend way too much time in &quot;what if we&#x27;re all just brains in jars?&quot;-land.

All colors exist. Color is not the same as wavelength, color is the human perception of a collection of one or more wavelengths of light. They are all real.

Yup. I had the same revelation when I learned that many of the colors we perceive don&#x27;t really &quot;exist&quot;. The closest thing to hue in nature is wavelength, but there is no wavelength for purple, for example. The color purple is our visual system&#x27;s interpretation of data (ratio of trichromatic cone cell activation). It doesn&#x27;t exist by itself.It&#x27;s the same reason that allows RGB screens to work. No screen has ever produced &quot;real&quot; yellow (for which there is a wavelength), but they still stimulate our trichromatic vision very similar to how actual yellow light would.

&gt; I imagine a sort of absolute reality that is the universe. And we’re all just sensor systems observing tiny bits of it in different and often overlapping ways.Fascinatingly this is pretty much what Advait Vedant (one interpretation of Hinduism) says.Alan Watts has talked a lot about this topic, if you’re (or anyone else is interested) his stuff is a more comfortable place to understand (compared to classical texts).

&gt; I imagine a sort of absolute reality that is the universe. And we’re all just sensor systems observing tiny bits of it in different and often overlapping ways.<a href="https:&#x2F;&#x2F;en.wikipedia.org&#x2F;wiki&#x2F;Phenomenology_(philosophy)" rel="nofollow">https:&#x2F;&#x2F;en.wikipedia.org&#x2F;wiki&#x2F;Phenomenology_(philosophy)</a><a href="https:&#x2F;&#x2F;en.wikipedia.org&#x2F;wiki&#x2F;Transcendental_idealism" rel="nofollow">https:&#x2F;&#x2F;en.wikipedia.org&#x2F;wiki&#x2F;Transcendental_idealism</a>

I studied remote sensing in undergrad and it really helped me grok sensors and signal processing. My favourite mental model revelation to come from it was that what I see isn’t the “ground truth.” It’s a view of a subset of the data. My eyes, my cat’s eyes, my cameras all collect and render different subsets of the data, providing different views of the subject matter.It gets even wilder when perceiving space and time as additional signal dimensions.I imagine a sort of absolute reality that is the universe. And we’re all just sensor systems observing tiny bits of it in different and often overlapping ways.

Maybe it’s just me but I took one look at the unprocessed photo (the first one) and immediately knew it was a skinny Christmas tree.I’ve been staring at 16-bit HDR greyscale space for so long…

I love the look of the final product after the manual work (not the one for comparison). Just something very realistic and wholesome about it, not pumped to 10 via AI or Instagram filters.

Don&#x27;t you think you&#x27;re being a bit too pedantic? Nothing really &quot;sees&quot;. Eyes also do gather light and the brain analyzes the signals. We&#x27;ve invented a word for it but the word is a high level abstraction that could easily be applied to a camera sensor as well.

Right. the statement &quot;Here’s a photo of a Christmas tree, as my camera’s sensor sees it&quot; is incoherent.

Yeah this what I immediately think too any time I see an article like this. Adjustments like contrast and saturation are plausible to show before&#x2F;after, but before any sort of tone curve makes no sense unless you have some magic extreme HDR linear display technology (we don&#x27;t). Putting linear data into 0-255 pixels which are interpreted as SRGB makes no sense whatsoever. You are basically viewing junk. It&#x27;s not like that&#x27;s what the camera actually &quot;sees&quot;. The camera sees a similar scene to what we see with our eyes, although it natively stores and interprets it differently to how our brain does (i.e. linear vs perceptual).

When talking about range I mean not naively remapping it to fit display media, but compressing it to fill the range of the media in a way that achieves the desired look. (Accounting for non-linearity of colour vision is part of that mentioned in the article.)

In the article adjusting for the range makes quite a small difference compared to the other steps.

An unprocessed photo does not “look”. It is RGGB pixel values that far exceed any display media in dynamic range. Fitting it into the tiny dynamic range of screens by thrusting
throwing away data strategically (inventing perceptual the neutral grey point, etc.) is what actually makes sense of them, and what is the creative task.

That&#x27;s a cool walkthrough.I spent a good part of my career, working in image processing.That first image is pretty much exactly what a raw Bayer format looks like, without any color information. I find it gets even more interesting, if we add the RGB colors, and use non-square pixels.

The matrix step has 90s video game pixel art vibes.

I trawled through much of the research but as you’ve mentioned it seems to be known only in astrophotography and mobile devices or other similarly constrained hardware.

This stuff exists and it&#x27;s fairly well-studied. It&#x27;s surprisingly hard to find without coming across it in literature though, the universe of image processing is huge. Joint demosaicing, for example, is a decades-old technique [0] fairly common in astrophotography. Commercial photographers simply never cared or asked for it, and so the tools intended for them didn&#x27;t bother either. You&#x27;d find more of it in things like scientific ISP and robotics.[0] <a href="https:&#x2F;&#x2F;doi.org&#x2F;10.1145&#x2F;2980179.2982399" rel="nofollow">https:&#x2F;&#x2F;doi.org&#x2F;10.1145&#x2F;2980179.2982399</a>

Sure, and my camera can do bird eye detection and whatnot too, but that&#x27;s a very lightweight model running in-body. Probably just a fine-tuned variant of something like YOLO.I&#x27;ve seen only a couple of papers from Google talking about stacking multiple frames from a DSLR, but that was only research for improving mobile phone cameras.Ironically, some mobile phones now have more megapixels than my flagship full-frame camera, yet they manage to stack and digitally process multiple frames using battery power!This whole thing reminds me of the Silicon Graphics era, where the sales person would tell you with a straight face that it&#x27;s worth spending $60K on a workstation and GPU combo that can&#x27;t even texture map when I just got a Radeon for $250 that runs circles around it.One industry&#x27;s &quot;impossible&quot; is a long-since overcome minor hurdle for another.

&gt; Something that surprised me is that very little of the computation photography magic that has been developed for mobile phones has been applied to larger DSLRs. Perhaps it&#x27;s because it&#x27;s not as desperately needed, or because prior to the current AI madness nobody had sufficient GPU power lying around for such a purpose.Sony Alpha 6000 had face detection in 2014.

I&#x27;ve been studying machine learning during the xmas break, and as an exercise I started tinkering around with the raw Bayer data from my Nikon camera, throwing it at various architectures to see what I can squeeze out of the sensor.Something that surprised me is that very little of the computation photography magic that has been developed for mobile phones has been applied to larger DSLRs. Perhaps it&#x27;s because it&#x27;s not as desperately needed, or because prior to the current AI madness nobody had sufficient GPU power lying around for such a purpose.For example, it&#x27;s a relatively straightforward exercise to feed in &quot;dark&quot; and &quot;flat&quot; frames as extra per-pixel embeddings, which lets the model learn about the specifics of each individual sensor and its associated amplifier. In principle, this could allow not only better denoising, but also stretch the dynamic range a tiny bit by leveraging the less sensitive photosites in highlights and the more senstive ones in the dark areas.Similarly, few if any photo editing products do simultaneous debayering and denoising, most do the latter as a step in normal RGB space.Not to mention multi-frame stacking that compensates for camera motion, etc...The whole area is &quot;untapped&quot; for full-frame cameras, someone just needs to throw a few server grade GPUs at the problem for a while!

This is really interesting. I&#x27;ll be back after reading it.

I&#x27;ve always considered that Tim Jennison quote to be a reference to C.P. Snow&#x27;s &quot;The Two Cultures&quot; lecture. Steve Jobs&#x27; ambition for Apple to be &quot;where the Liberal Arts and Technology meet&quot; also seemed similarly influenced. If you haven&#x27;t read Snow&#x27;s lecture, it&#x27;s well worth the quick read.

This reminds me of a couple things:== Tim&#x27;s Vermeer ==Specifically Tim&#x27;s quote &quot;There&#x27;s also this modern idea that art and technology must never meet - you know, you go to school for technology or you go to school for art, but never for both... And in the Golden Age, they were one and the same person.&quot;<a href="https:&#x2F;&#x2F;en.wikipedia.org&#x2F;wiki&#x2F;Tim%27s_Vermeer" rel="nofollow">https:&#x2F;&#x2F;en.wikipedia.org&#x2F;wiki&#x2F;Tim%27s_Vermeer</a><a href="https:&#x2F;&#x2F;www.imdb.com&#x2F;title&#x2F;tt3089388&#x2F;quotes&#x2F;?item=qt2312040" rel="nofollow">https:&#x2F;&#x2F;www.imdb.com&#x2F;title&#x2F;tt3089388&#x2F;quotes&#x2F;?item=qt2312040</a>== John Lind&#x27;s The Science of Photography ==Best explanation I ever read on the science of photography
<a href="https:&#x2F;&#x2F;johnlind.tripod.com&#x2F;science&#x2F;scienceframe.html" rel="nofollow">https:&#x2F;&#x2F;johnlind.tripod.com&#x2F;science&#x2F;scienceframe.html</a>== Bob Atkins ==Bob used to have some incredible articles on the science of photography that were linked from photo.net back when Philip Greenspun owned and operated it. A detailed explanation of digital sensor fundamentals (e.g. why bigger wells are inherently better) particularly sticks in my mind. They&#x27;re still online (bookmarked now!)<a href="https:&#x2F;&#x2F;www.bobatkins.com&#x2F;photography&#x2F;digital&#x2F;size_matters.html" rel="nofollow">https:&#x2F;&#x2F;www.bobatkins.com&#x2F;photography&#x2F;digital&#x2F;size_matters.h...</a>

It&#x27;s not really - it&#x27;s going in the inverse direction regarding how much more processed and artificially altered it gets.

Except it seems to be going in the opposite direction, every phone I&#x27;ve upgraded (various Androids and iPhones) seemed to have more smoothing than the one I&#x27;d had before. My iPhone 16 night photos look like borderline generative AI and there&#x27;s no way to turn that off!I was honestly happier with the technically inferior iPhone 5 camera, the photos at least didn&#x27;t look fake.

Just wait a few years, all of this is still getting better.

Equally bad is the massive over sharpening applied to CCTV and dash cams. I tried to buy a dash cam a year ago that didn&#x27;t have over sharpened images but it proved impossible.Reading reg plates would be a lot easier if I could sharpen the image myself rather than try to battle with the &quot;turn it up to 11&quot; approach by manufacturers.

&gt; For example, the IP cameras I have, sometimes when I come home on the bike, part of the wheel is missing, having been deleted by the algorithm as it struggled with the &quot;grainy&quot; asphalt driveway underneath.Heavy denoising is necessary for cheap IP cameras because they use cheap sensors paired with high f-number optics. Since you have a photography background you&#x27;ll understand the tradeoff that you&#x27;d have to make if you could only choose one lens and f-stop combination but you needed everything in every scene to be in focus.You can get low-light IP cameras or manual focus cameras that do better.The second factor is the video compression ratio. The more noise you let through, the higher bitrate needed to stream and archive the footage. Let too much noise through for a bitrate setting and the video codec will be ditching the noise for you, or you&#x27;ll be swimming in macroblocks. There are IP cameras that let you turn up the bitrate and decrease the denoise setting like you want, but be prepared to watch your video storage times decrease dramatically as most of your bits go to storing that noise.&gt; Smartphone and dedicated digital still cameras aren&#x27;t as drastic, but when zoomed in, or in low light, faces have a &quot;painted&quot; kind of look. I&#x27;d prefer honest noise, or better yet an adjustable denoising algorithm from &quot;none&quot; (grainy but honest) to what is now the default.If you have an iPhone then getting a camera app like Halide and shooting in one of the RAW formats will let you do this and more. You can also choose Apple ProRAW on recent iPhone Pro models which is a little more processed, but still provides a large amount of raw image data to work with.

What phone it was? I am sure that there is a lot of ML involved to figure out how to denoise photos in the dark, etc., but I never noticed anything that I&#x27;d want to describe as &quot;AI filter&quot; on my photos.

I hear you. Two years ago I went to my dad&#x27;s and I spent the afternoon &quot;scanning&quot; old pictures of my grandparents (his parents), dead almost two decades ago. I took pictures of the physical photos, situating the phone as horizontal as possible (parallel to the picture), so it was as similar as a scan (to avoid perspective, reflection, etc).It was my fault that I didn&#x27;t check the pictures while I was doing it. Imagine my dissapointment when I checked them back at home: the Android camera decided to apply some kind of AI filter to all the pictures. Now my grandparents don&#x27;t look like them at all, they are just an AI version.

It&#x27;s like food: Virtually all food is &quot;processed food&quot; because all food requires some kind of process before you can eat it. Perhaps that process is &quot;picking the fruit from the tree&quot;, or &quot;peeling&quot;. But it&#x27;s all processed in one way or another.

&gt; does applying the same transfer function to each pixel (of a given colour anyway) count as “processing”?This is interesting to think about, at least for us photo nerds. ;) I honestly think there are multiple right answers, but I have a specific one that I prefer. Applying the same transfer function to all pixels corresponds pretty tightly to film &amp; paper exposure in analog photography. So one reasonable followup question is: did we count manually over- or under-exposing an analog photo to be manipulation or “processing”? Like you can’t see an image without exposing it, so even though there are timing &amp; brightness recommendations for any given film or paper, generally speaking it’s not considered manipulation to expose it until it’s visible. Sometimes if we pushed or pulled to change the way something looks such that you see things that weren’t visible to the naked eye, then we call it manipulation, but generally people aren’t accused of “photoshopping” something just by raising or lowering the brightness a little, right?When I started reading the article, my first thought was, ‘there’s no such thing as an unprocessed photo that you can see’. Sensor readings can’t be looked at without making choices about how to expose them, without choosing a mapping or transfer function. That’s not to mention that they come with physical response curves that the author went out of his way to sort-of remove. The first few dark images in there are a sort of unnatural way to view images, but in fact they are just as processed as the final image, they’re simply processed differently. You can’t avoid “processing” a digital image if you want to see it, right? Measuring light with sensors involves response curves, transcoding to an image format involves response curves, and displaying on monitor or paper involves response curves, so any image has been processed a bunch by the time we see it, right? Does that count as “processing”? Technically, I think exposure processing is always built-in, but that kinda means exposing an image is natural and not some type of manipulation that changes the image. Ultimately it depends on what we mean by “processing”.

Was mentioning to my GF (non technical animator) about the submission Clock synchronization is a nightmare. And how it comes up like a bad penny. She said in animation you have the problem that you&#x27;re animating to match different streams and you have to keep in sync. Bonus you have to dither because if you match too close the players can smell it&#x27;s off.Noise is part of the world itself.

&gt; But does applying the same transfer function to each pixel (of a given colour anyway) count as &quot;processing&quot;?In some sense it has to, because you can include a parametric mask it that function which makes it possible to perform local edits with global functions.

&gt; Smartphone and dedicated digital still cameras aren&#x27;t as drastic, but when zoomed in, or in low light, faces have a &quot;painted&quot; kind of look.My theory is that this is trying to do denoising after capturing the image with a high ISO. I personally hate that look.On my dedicated still camera I almost always set ISO to be very low (ISO 100) and only shoot people when lighting is sufficient. Low light is challenging and I’d prefer not to deal with it when shooting people, unless making everything dark is part of the artistic effect I seek.On the other hand on my smartphone I just don’t care that much. It’s mostly for capturing memories in situations where bringing a dedicated camera is impossible.

But does applying the same transfer function to each pixel (of a given colour anyway) count as &quot;processing&quot;?What bothers me as an old-school photographer is this. When you really pushed it with film (e.g. overprocess 400ISO B&amp;W film to 1600 ISO and even then maybe underexpose at the enlargement step) you got nasty grain. But that was uniform &quot;noise&quot; all over the picture. Nowadays, noise reduction is impressive, but at the cost of sometimes changing the picture. For example, the IP cameras I have, sometimes when I come home on the bike, part of the wheel is missing, having been deleted by the algorithm as it struggled with the &quot;grainy&quot; asphalt driveway underneath.Smartphone and dedicated digital still cameras aren&#x27;t as drastic, but when zoomed in, or in low light, faces have a &quot;painted&quot; kind of look. I&#x27;d prefer honest noise, or better yet an adjustable denoising algorithm from &quot;none&quot; (grainy but honest) to what is now the default.

I work with camera sensors and I think this is a good way to train some of the new guys, with some added segments about the sensor itself and readout. It starts with raw data, something any engineer can understand, and the connection to the familiar output makes for good training.

We should blame English&#x27;s crazy spelling system, not the author.

The author makes this error every single time, in both articles by him I&#x27;ve read today. For some reason, as a person whose native language is not English, this particular error pisses me off so much.

&gt; As a result of this, if the linear data is displayed directly, it will appear much darker then it should be.Then -&gt; than? (In case the author is reading comments here.)

Not quite. At the very least, the RAW file is a static file. Whereas your camera chip can make interactive decisions.In any case, RAW files aren&#x27;t even all that raw. First, they are digitised. They often apply de-noising, digital conditioning (to take care of hot and dead pixels), lens correction. Some cameras even apply some lossy compression.

You can always edit the RAW files from the camera, which essentially means working with the same data the camera chip had to generate the JPEGs.

&gt; There’s nothing that happens when you adjust the contrast or white balance in editing software that the camera hasn’t done under the hood. The edited image isn’t “faker” then the original: they are different renditions of the same data.Almost, but not quite? The camera works with more data than what&#x27;s present in the JPG your image editing software sees.

I appreciate the authors honest with their astrophotography.

They did:&gt; Sensor data with the 14 bit ADC values mapped to 0-255 RGB.

Author should&#x27;ve mentioned how the first image &quot;as my camera’s sensor sees it&quot; was obtained.

I&#x27;ve had problems like this before, but I always attributed it to auto white balance. That great ruiner of sunset photos the world over.

I remember when they did this to pictures of the moon: <a href="https:&#x2F;&#x2F;arstechnica.com&#x2F;gadgets&#x2F;2023&#x2F;03&#x2F;samsung-says-it-adds-fake-detail-to-moon-photos-via-reference-photos" rel="nofollow">https:&#x2F;&#x2F;arstechnica.com&#x2F;gadgets&#x2F;2023&#x2F;03&#x2F;samsung-says-it-adds...</a>

This is VERY interesting and I am glad you posted this, as it is my first time coming across this.

Huh wonder which camera apps enable use of this API?

To disambiguate, this is not meant as “added a mode to the stock Camera app”, but rather “added a mode to the camera API that iOS developers can use”.

Thank you for sharing. Seems to validate my suspicions!

iOS added a camera mode for medical photos that extra doesn&#x27;t do that.<a href="https:&#x2F;&#x2F;developer.apple.com&#x2F;videos&#x2F;play&#x2F;wwdc2024&#x2F;10162&#x2F;" rel="nofollow">https:&#x2F;&#x2F;developer.apple.com&#x2F;videos&#x2F;play&#x2F;wwdc2024&#x2F;10162&#x2F;</a>

You may know that intermittent rashes are always invisible in the presence of medical credentials.Years ago I became suspicious of my Samsung Android device when I couldn&#x27;t produce a reliable likeness of an allergy induced rash. No matter how I lit things, the photos were always &quot;nicer&quot; than what my eyes recorded live.The incentives here are clear enough - people will prefer a phone whose camera gives them an impression of better skin, especially when the applied differences are extremely subtle and don&#x27;t scream airbrush. If brand-x were the only one to allow &quot;real skin&quot; into the gallery viewer, people and photos would soon be decried as showing &#x27;x-skin&#x27;, which would be considered gross. Heaven help you if you ever managed to get close to a mirror or another human.To this day I do not know whether it was my imagination or whether some inline processing effectively does or did perform micro airbrushing on things like this.Whatever did or does happen, the incentive is evergreen - media capture must flatter the expectations of its authors, without getting caught in its sycophancy. All the while, capacity improves steadily.

Surprised that nobody else commented on this, it is a very sad tree.

Did you steal that tree from Charlie Brown?

Interesting to see this whole thing shown outside of Astrophotography, sometimes I forget it&#x27;s the same stuff!

2 top HN posts in 1 day, maurycyz is on fire!

I was lucky enough to take some introductory courses at the NTNU Colorlab in Gjøvik, Norway. What I learned there changed my view on vision.Computer imaging is much wider than you think. It cares about the entire signal pipeline, from emission from a light source, to capture by a sensor, to re-emission from a display, to absorption in your eye, and how your brain perceives it. Just like our programming languages professor called us &quot;Pythonized minds&quot; for only knowing a tiny subset of programming, there is so much more to vision than the RGB we learn at school. Look up &quot;Metamerism&quot; for some entry-level fun. Color spaces are also fun and funky.There are a lot of interesting papers in the field, and its definitely worth reading some.A highlight of my time at university.

I have always wondered at the lowest level how a camera captures and processes photos. Much appreciated post.

I fill the original photo to Nano banana Pro, and it recovered well. It also explained how to recover it.

Really enjoyed the article, thanks!
A small nit - I think you have a small mistake in the value range at the start - 136000 should probably be 13600?

&gt;No adblocker detected. Consider using an extension like uBlock Origin to save time and bandwidth. Click here to close.So cute (I am running a DNS adblock only, on the work browser)

This is a great article but I was surprised how anemic the tree was :)Really good article though

&gt;Our perception of brightness is non-linear.Apart from brightness, it&#x27;s everything. Loudness, temperature, etc.

see also: <a href="https:&#x2F;&#x2F;vas3k.com&#x2F;blog&#x2F;computational_photography&#x2F;" rel="nofollow">https:&#x2F;&#x2F;vas3k.com&#x2F;blog&#x2F;computational_photography&#x2F;</a>

Highly recommend this CaptainDisillusion video that covers this topic of how cameras process colors in a very entertaining way<a href="https:&#x2F;&#x2F;www.youtube.com&#x2F;watch?v=aO3JgPUJ6iQ" rel="nofollow">https:&#x2F;&#x2F;www.youtube.com&#x2F;watch?v=aO3JgPUJ6iQ</a>

Hey thanks a bunch. These were not the ones though. The blog was more mathy and had a signal processing analysis and fixed point analysis of the repeated blue and sharpen phenomena.I had upvoted it on HN either as a post or a comment that had the link. Wish there was an easy way to search through ones own upvoted comments and posts.Thanks again though for trying to help.

Maybe these?<a href="https:&#x2F;&#x2F;patorjk.com&#x2F;blog&#x2F;2025&#x2F;11&#x2F;02&#x2F;what-happens-if-you-blur-and-sharpen-and-image-1000-times&#x2F;" rel="nofollow">https:&#x2F;&#x2F;patorjk.com&#x2F;blog&#x2F;2025&#x2F;11&#x2F;02&#x2F;what-happens-if-you-blur...</a><a href="https:&#x2F;&#x2F;patorjk.com&#x2F;blog&#x2F;2025&#x2F;03&#x2F;10&#x2F;making-a-follow-up-to-subpixel-snake&#x2F;" rel="nofollow">https:&#x2F;&#x2F;patorjk.com&#x2F;blog&#x2F;2025&#x2F;03&#x2F;10&#x2F;making-a-follow-up-to-su...</a><a href="https:&#x2F;&#x2F;relativisticobserver.blogspot.com&#x2F;2012&#x2F;02&#x2F;keeping-it-cool.html" rel="nofollow">https:&#x2F;&#x2F;relativisticobserver.blogspot.com&#x2F;2012&#x2F;02&#x2F;keeping-it...</a>

Does anyone remember a blog post on how repeated sharpening and blurring results in reaction diffusion Turing patterns. That blog also had an article on sub pixel shift.Trying frantically to remember and looking for it in my bookmarks but failing miserably. If anyone remembers what blog I am talking about please leave a link.

That poor Christmas tree. Whatever happened to it?

&gt; On it’s own, this would make the LED Christmas lights into an overstaturated mess,So, realistic then?

Now how do I apply this to get the most realistic looking shaders in 3d?

That reminds me of the explanation of why sometimes you look at the second hand of a clock and it seems like it takes longer than a second to tick- because your brain is actually (IIRR) delaying and extending the time it sends the image (I think)

Your brain does a far more impressive job of fooling you into believing that the image you see of your surroundings in your brain is actually what your sensory apparatus is seeing. It very much isn’t. Just the mechanism to cope with your eye movement without making you woozy is, by itself, a marvel.Our brains are far more impressive than what amounts to fairly trivial signal processing done on digital images.

Ok I just never imagined that photons hitting camera lenses would not produce a “raw” image that made sense to my eyes - I am stunned and this is a fantastic addition to the canon of things one should know about the modern world.(I also just realised that the world become more complex than I could understand when some guy mixed two ochres together and finger painted a Woolly Mammoth.)

Nice illustration.To be a bit picky, there’s no unprocessed photo. They start with a minimally processed photo and take it from there.The reason I clicked is that when I saw the title, I’m tempted to think they might be referring to analog photo (ie film). In that case I think there’s a well defined concept of “unprocessed” as it is a physical object.For digital photo, you require at least a rescaling to turn it to grayscale as the author did. But even that, the values your monitor shows already is not linear. And I’m not sure pedagogically it should be started with that, as the authors mention later about the Bayer pattern. Shouldn’t “unprocessed” come with the color information? Because if you start from gray scale, the color information seems to be added from the processing itself (ie you’re not gradually adding only processing to your “unprocessed” photo).To be fair, representing “unprocessed” Bayer pattern is much harder as the color filter does not nicely maps to RGB. If I were to do it I might just map the sensor RGB to just RGB (with default color space sRGB) and make a footnote there.

If you&#x27;re making a post like this, why not put a color calibration chart in the image?

Do you have any specific book recommendations for a layman?

For anyone that enjoyed this, pick up a book on digital image processing. The first chapters of most such books cover this, in almost step-by-step fashion. And then the books will usually start to venture into more classical machine learning stuff.

This post reminded me of the blog posts [0] regarding the &quot;megapixels&quot; camera app for the pinephone , written by Martijn Braam.
For those interested it dives quite deep into the color profiling and noise reduction and more to make the pinephone camera usefull.[0] <a href="https:&#x2F;&#x2F;blog.brixit.nl&#x2F;tag&#x2F;megapixels&#x2F;" rel="nofollow">https:&#x2F;&#x2F;blog.brixit.nl&#x2F;tag&#x2F;megapixels&#x2F;</a>

&gt; For comparison, here’s the image my camera produced from the same data:Is it just me or does his version look a lot better? The camera version has brighter colors in some places (the star and lights), but it seems to be at the cost of detail and contrast in the subtler areas.

And here&#x27;s the blog article describing the widget: <a href="https:&#x2F;&#x2F;maurycyz.com&#x2F;misc&#x2F;ads&#x2F;" rel="nofollow">https:&#x2F;&#x2F;maurycyz.com&#x2F;misc&#x2F;ads&#x2F;</a>

&quot;No adblocker detected - consider using uBlock Origin&quot;I am getting a real sensation of what can only be described as warm fuzzies. I may have fallen deeply in love with this blog.

Another tool to add to my arsenal of responses to people who claim either &quot;no filter used&quot; or &quot;SOOC photo&quot;. Both of those may be true for some values of &quot;no filter&quot; or &quot;straight out of camera&quot; but they&#x27;re not remotely describing the reality that any digital image is heavily manipulated before it leaves the camera. And that&#x27;s ok! Our eyes are filters. Our brain is a filter. Photographic film and processing techniques are filters. The use of &quot;no filter&quot; and &quot;SOOC&quot; to imply capturing something unedited and therefore authentic is the artificial thing.

Related to the final photo: you might feel like those Christmas lights feel too blue compared to your nostalgic version of Christmas. This is because LEDs can easily achieve a brighter blue than old incandescent Christmas lights which used color filters on a white light.Technology Connections vid: <a href="https:&#x2F;&#x2F;youtu.be&#x2F;va1rzP2xIx4" rel="nofollow">https:&#x2F;&#x2F;youtu.be&#x2F;va1rzP2xIx4</a>

Bear with me for a possibly strange question, directed more towards chemists. Are their crystalline compounds with the formula X_2YZ where X,Y,Z are three elements of roughly same atomic size.What I am curious if are the different symmetrical arrangements chosen by such crystals and how they compare with Bayer pattern. The analogy being X becomes a site for green and the other two for red and blue.

It&#x27;s not really grayscale. The output of an image sensor integrated circuit is a series of voltages read one after the other, that could be from –0.3 to +18 volts for example, in an order specific to the sensor&#x27;s red, green and blue &quot;pixels&quot; arrangement. The native picture (fig 1) is the result of converting a sensor&#x27;s output voltage to a series of values from black (let&#x27;s say -0.3 volts for example) up to white (let&#x27;s say +18 volts for example) while ignoring if they are from a red, a green or a blue image sensor &quot;pixel&quot;.The various &quot;raw&quot; camera image formats kind of work like this, they include the voltages converted to some numerical range and what each &quot;pixels&quot; represents for a specific camera sensor setup.

It&#x27;s just a common default choice to represent spacial data that lacks any context on how to interpret the values chromatically. You could very well use a heatmap-like color scheme instead.

They’re known as DNs, or digital numbers. Thom Hogan’s eBooks do a phenomenal job of explaining the intricacies of camera sensors, their architecture, processing to JPEGs, and pretty much every aspect of capturing good photos.The books, while geared toward Nikon cameras, are generally applicable. And packed with high-quality illustrations and an almost obsessive uber-nerd level of detail. He’s very much an engineer and photographer. When he says “complete guide”, he means it.The section on image sensors, read-outs,
and ISO&#x2F;dual gain&#x2F;S&amp;R, etc. is particularly interesting—-and should be baseline knowledge for anyone who’s seriously interested in photography.[0] <a href="https:&#x2F;&#x2F;zsystemuser.com&#x2F;z-system-books&#x2F;complete-guide-to-the-nikon-4.html" rel="nofollow">https:&#x2F;&#x2F;zsystemuser.com&#x2F;z-system-books&#x2F;complete-guide-to-the...</a>

Why is the native picture (fig 1) in grayscale? or more generally why is black and white the default of signal processing? Is it just because black and white are two opposites that can be easily discerned?

What an unprocessed photo looks like