Halide embraces photo purists with new anti-AI processing mode
9to5mac.com
9to5mac.com
which uses ML for denoising high ISO shots was like getting a sun in my pocket for indoor sports photography. You will take ML developers out of my cold dead hands.
On the Internet, Waifu2x has proven popular for quite some time. I don't know how it works architecturally, but they've trained an ML model on anime-style illustrations and photos, specifically to upscale and denoise images (particularly, to reverse JPEG artifacts) using a corpus of images before and after downscaling/adding JPEG artifacts. It is incredible when just using it to denoise JPEG and quite impressive for scaling up to 2x. It definitely works better for anime-style illustrations, which suffer from JPEG artifacts more than photographs do, anyhow.
I also like Google Camera's "Night Sight" feature. It's maybe not astounding anymore, but it definitely was a vast improvement for capturing photos at night using a little smartphone camera when it first came around.
That said, there are pitfalls to these more advanced algorithms. They can have different failure modes than people are used to. People routinely fail to realize how dangerous it can be when a machine is "lying" to you in a way that you can't necessarily comprehend the risks of; even before ML we have plenty of good examples, like Xerox machines performing compression that accidentally altered the numbers on the page[1]. With ML algorithms that pull increasingly more signal out of increasingly less information, the potential for bad extrapolations and downright hallucination certainly must increase. There's been some funny examples of this with the iPhone and Google Camera features, but it really does have some interesting implications. Can we always trust these photos to be legally-admissible, for example, even when they're not altered intentionally? Don't know the answer. It's probably not a huge deal, but at some point, this will surely become an issue, and I bet it will be very interesting (and hopefully not too tragic.)
[1]: https://www.dkriesel.com/en/blog/2013/0802_xerox-workcentres...?
Now I see papers where people train superesolution models on pictures of healthy tissues and tumors and dodn’t seem bothered at all that the model may know how to hallucinate both.
Sadly, most cameraphones are now just applying ML before handing the data out to the APIs. Getting the raw sensor data from some phones is literally impossible for third party apps now, as I understand it (looking at you Samsung).
http://yager.io/comp/comp.html
https://petapixel.com/2023/02/04/the-limits-of-computational...
Tangentially related: I learned today that an old point-and-shoot 1-inch camera I'd bought over 7 years ago is now selling for twice the MRSP used online, apparently because it looks cool/retro and/or because photos and videos coming out of it don't look "pre-processed" in any way.
In 2025 I'd love to see "AI" disappear from usage. I know I'm not likely to get that, but damn if I am not tired of hearing about it. I've never wanted a dumb phone more than I have in 2024, or to get rid of my computers.
And that's not even getting into the fun things we can do by applying our own radiation to reflect off the object! We can do a lot with good lighting.
It is nice to have the ability to choose to take more control over automated steps of the process though, especially because modern apple phones have impressive cameras.