image-super-resolution

Pixelmator Pro[1] does a pretty good job with its "ML Super Resolution". Apparently Adobe have a similar "Super Resolution"[2]. One of the VQGAN-CLIP notebooks uses ISR[3] (but I haven't managed to get that working locally yet because of weird tensorflow version requirements.) [1] https://www.pixelmator.com/pro/ [2] https://photographylife.com/reviews/adobe-super-resolution [3] https://github.com/idealo/image-super-resolution

The flicker would be a challenge but you can use something like DeOldify [1] or super resolution [2] on the frame sequence to improve the colour and resolution... although you may get some weird artifacts between frames using image models rather than models built for video. Generally video models are quite far behind image models because they're so much higher dimensional (much more computationally expensive and expensive to annotate) but I'm sure it's a matter of time before someone releases a DeOldify type model for video (if one doesn't exist already). [1] https://github.com/jantic/DeOldify [2] https://github.com/idealo/image-super-resolution

We just released a major update of our Keras based image super resolution project. Now you can super-scale your images and run experiments very easily with RDNs and GANs. In particular, ⭐️ We've added some new cool stuff like VGG deep features + GANs to achieve amazingly realistic upscaled images ⭐️ We provide a few more pre-trained weights as well as some Colab-notebook tutorials to play around ⭐️ And the best thing our project is now available on PyPI! So you can just pip install your way to our project If you want to know all details and results of the new update, check out our new blog post and also have a look at the documentation. - Blog: https://medium.com/idealo-tech-blog/zoom-in-enhance-a-deep-l... - Documentation: https://idealo.github.io/image-super-resolution/ - Github: https://github.com/idealo/image-super-resolution/ - Colab