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Comment by _justinfunk | Hacker News Reader

The actual example images in the article look... bad?

Maybe someone who understands optics better could help explain why they look unnatural. Is it an uncanny-valley analogue?

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I believe what you are referring to is aberrations? https://expertphotography.com/lens-aberrations-photography/

Uncanny valley is more your mind not understanding what is being perceived, this is more that the image is not as sharp due to imperfections in the lens.

If the lens were perfect it would end up looking like any other picture you see with everything in focus. This is achieved today by stacking say, 3 pictures on top of each other where each picture has a different part of the subject in focus

That link took me to a description of how women should be posed with an "S-shaped figure".

I get what you’re saying about uncanny valley. My first thought when I saw the example image was “maybe it looks weird because my brain expects some parts to be out-of-focus?”

I have no idea if that’s true or if it’s just a bad looking picture. The sky looks weird.

It's reconstructed from sub-images, the reconstruction algorithm can probably be improved.

The lack of focal blur prevents depth perception, and the objects don't have "natural scale" clues (like a human or a car) so the photo looks like a flat 2-D collage.

We need eye tracking that would blur the objects that would normally be out of focus for what the person is looking at.

My opinion is that finished images from professional photographers are cleaned up interpretations. And increasingly so on an automated basis from smart phone image processing stacks.

Raw images often don't look as good to the eye, even if they contain fairly complete information that a professional or automation can work with to provide a number of different finishing approaches that are pleasing to human perceptions.

This techs raw capture is significantly different from current image sensor data, and their processed image is mostly making it close to a mature "raw" image is sort of how I take the sample photos in the article.

I agree with you about modern phone cameras, but I have an old digital with minimal postprocessing, and the images come out clear (at a whopping 3 megapixels), as do the raws on my Sony A7. Similarly, film cameras worked fine without piles of neural networks, etc during development.

The images in the article are all blurry, but seem decent for a research prototype. Presumably, dumping lots of commercial development resources into the technology will improve it.

I think I disagree with your opinion. Raw images often look fantastic if they are captured properly (in focus, exposed correctly). I still work regularly with old film cameras, and the photos develop very very nicely without any digital alterations. When I shoot with my digital I usually save both RAW and JPEG formats so I have the extra raw data if I need it but if I've taken a good shot I minimize my digital alterations.

There are some additional tweaks that can be done to a "raw" format camera image that contains extra image data compared to a processed/jpeg type flatter image, but there's no reason they should look bad coming out of the camera. Maybe unbalanced white color values at most.

Raw camera data requires an interpretation to be displayed on a monitor at all and usually needs a lot of processing to produce a natural-looking photo on the screen. Demosaicing at the very least but gamma curve correction, color response correction, etc. as well.

We rely a LOT on focus to get depth cues from 2D images.

When everything is made to be in focus, we lose depth cues and everything looks wrong.

That said, they certainly could have created better example images. Like, I would have placed an object like a figurine on an outdoor table with a city skyline in the background and taken a picture of the object from about a foot away. Normally, this would put the object in focus with the city being blurred, as well as part of the table. It would have made it very clear what they were going for.

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