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I'd like to see some examples showing how it does when taking a picture of completely random fractal noise. That should show it's not just trained to reconstruct known image patterns.

Generally it's probably wise to be skeptical of anything that appears to get around the diffraction limit.

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brookst 4 days ago | parent [-]

I believe the claim is that the NN is trained to reconstruct pixels, not images. As in so many areas, the diffraction limit is probabalistic so combining information from multiple overlapping samples and NNs trained on known diffracted -> accurate pairs may well recover information.

You’re right that it might fail on noise with resolution fine enough to break assumptions from the NN training set. But that’s not a super common application for cameras, and traditional cameras have their own limitations.

Not saying we shouldn’t be skeptical, just that there is a plausible mechanism here.

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Intralexical 4 days ago | parent | next [-]

My concern would be that if it can't produce accurate results on a random noise test, then how do we trust that it actually produces accurate results (as opposed to merely plausible results) on normal images?

Multilevel fractal noise specifically would give an indication of how fine you can go.

	▲	brookst 3 days ago \| parent [-]
		"Accurate results" gets you into the "what even is a photo" territory. Do today's cameras, with their huge technology stack, produce accurate results? With sharpening and color correction and all of that, probably not. I agree that measuring against such a test would be interesting, but I'm not sure it's possible or desirable for any camera tech to produce an objectively "true" pixel by pixel value. This new approach may fail/cheat in different ways, which is interesting but not disqualifying to me.

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neom 4 days ago | parent | prev [-]

we've had very good chromatic aberration correction since I got a degree in imaging technology and that was over 20 years ago so I'd imagine it's not particularly difficult for name your flavour of ML.