[colincooke]

This whole concept is so reckless in realms where the image content actually matters and people keep doing it anyways. You cannot CREATE information. You can infer it in certain situations, but if you infer the information and then analyze it you are setting yourself up to make mistakes by overextrapolating a bias/trend in your data to images where you have no idea if that inference is valid.

This was a big thing in the medical imaging community (where I did my stint as a CV researcher), folks were hallucinating microscope images and CT scans with no information theory justification as to why it worked.

Super resolution IS possible, but it must be done by synthesizing new pieces of information, not by inferring based on what other similar looking objects looked like. A cool technique by my former advisor does this with microscopes [1].

Deep learning has a place here, just not as a "lets create information" step, but as a way to learn how to synthesize additional information about images from more sources (i.e. more similar to how Google does Night Sight [2]).

Edit: if you want to see (an attempt) at using deep learning in this field you can checkout one of my papers [3].

[1]: https://en.wikipedia.org/wiki/Fourier_ptychography [2]: http://graphics.stanford.edu/papers/night-sight-sigasia19/ni... [3]: https://openaccess.thecvf.com/content/ICCV2021/html/Cooke_Ph...

[psychphysic]

This is all very sensible criticism but a bit generic.

Sometimes detail accuracy doesn't matter but the presence does.

Just about every image you ever view has had some manipulation applied. Sometimes that results in a "better" image.

Consider all astronomical images for human consumption, even smartphones adapt now to skin tone.

I'm playing hogwarts legacy, a recent AAA game which is very demanding, and where aesthetics are very important on a mediocre PC precisely because FSR from AMD (and if I had an Nvidea GPU DLSS and DLAA).

[colincooke]

I should have been more nuanced I suppose. There is a time and place for these kinds of image "enhancements", they just don't belong in ESRI's scientific GIS platform. Folks don't view these images for pleasure (or at least very few do), they are typically used to analyze the satellite data or georeference other imagery.

Deep learning image enhancement is totally appropriate in your smartphone, as there the goal is not accuracy but perceived quality. Doing this to satellite imagery where the primary consumer cares about accuracy is what I call "reckless"

[mistrial9]

just wait for the 'real time enhancement' of drone cameras for critical security applications. Snark aside, it is very reasonable and competitive to want to do color correction, focus and shadow darkness on-the-fly; secondly raw sensing data is very large, but on-the-fly capture is bandwidth-sensitive, so very clever compression and band reduction is also desirable and indeed competitive.

What happens if there are multi-million dollar economic outcomes depending on the details of the remote sensing content, as in disaster response.

[psychphysic]

Fair point, hopefully it'll be targeted appropriately.

I vaguely remember the Rittenhouse trial had an expert discuss if pinch-to-zoom could introduce false information.

[m2fkxy]

Agree, you can even see on their input/predicted/target examples that the created/invented data is off enough from ground truth to be in some cases unsuitable for photo interpretation.

[sbaiddn]

In the Rittenhouse trial this issue came up, namely the faithfulness of digital images and how computers manipulated images.