Neural Enhance – Super Resolution for images using deep learning (opens in new tab)

I recall watching a movie that was converted from black-and-white to color as a child. There were many distracting artifacts. Most notable was the hairlines of the actors would shift as the actor rotated their head. It made the film unwatchable.

I have a side business doing Film restoration and am not aware of any solution like that. Probably the best upscale solution there is is from Teranex, acquired by BlackMagic Design. Evertz probably also has something in their offering.

In theory you could use this to increase temporal resolution as well. Turn 24 fps movies into 60 fps, and upscale regular HD to 4k.

I've seen some videos from Cold War satellite photo analysts, and the way they can look at some tiny gray blobs and go "That's a T-64 tank, that's a T-62 tank, that's an SA2 launcher" etc.

However, they can make things more easily interpretable by humans. A rough analogy is turning up the contrast - given a very dark image of licence plate where the black parts are totally black (#000000) and white parts are just very dark (#010101), the characters definitely can be recognized even while human in normal conditions would just see it as totally black, and processing would help.

Most other approaches don't even try to inject high-frequency detail into the high-resolution images because the PSNR/SSIM benchmarks drop. Until those metrics/benchmarks are dropped, there'll be little more progress in super-resolution.

http://imgur.com/a/moP57

If you scroll down on GitHub to see the faces examples, those are achieved by a domain-specific network. I suspect you'll similarly get extremely high-quality if you have good input images.

On GitHub, below each GIF there's a demo comparison, but on the site you can also submit your own to try it out (click on title or restart button). Takes about 60s currently; running on CPU as GPUs are busy training ;-)