I’ve been recommended by a smart dude something that may be even better: ESRGAN/BasicSR. It’s an AI-upscaling algorithm just like Gigapixel, but you can train it with your own datasets, so if you have a representative dataset of what you’re trying to upscale (you need high res and low res reference for training), you can get really incredible results.
I’m currently playing around with it, and while training is very slow (we’re talking many days), it’s impressive what it can do imo.
I’ve tried some pre-trained ESRGAN upscalers, and I’d say it is a more natural looking upscale than ai gigapixel, but I’d still say ai gigapixel wins in the detail, even if it looks a bit too sharp. And I’d still say in terms of noise reduction ai gigapixel and waifu2x are better at removing compression artifacts. Of course waifu2x doesn’t try and add or recover detail, but simply a high resolution upscale that retains all the original detail.
All three are really good tbh, and all have their purposes. I’d say a mix of them is ideal.
See, that’s where your own training data comes into play. Gigapixel is trained on a generic dataset you know nothing about. Of course they have probably put lots of effort into a good curated dataset for a good product, more work arguably than people working for free have done for ESRGAN. Gigapixel may be good at noise reduction, but it’s not perfect, because it may not apply perfectly to whatever dataset you yourself have. To give an example, I’m currently training ESRGAN to upscale old roq game videos (Quake 3 engine). For that I took a video I have in high quality (Prores) and made roq encodes at similar bitrates to the game videos. Then I put that into the training. I’m not far into the training yet (only 2 days or so), but it seems promising. I tried a screenshot from one of those videos in Gigapixel and while it was sharp, it misinterpreted a lot of the artifacts as detail, and I think that with enough training, ESRGAN will be much smarter about upscaling this particular kind of source.