Awesome-Deblurring VS Ghost-DeblurGAN

This is a really tough one. I'm new to this and haven't made any progress after an hour. Here are some resources though: https://github.com/subeeshvasu/Awesome-Deblurring

This is an ongoing area of study. Here's a list of research (ML-focused) on the topic - https://github.com/subeeshvasu/Awesome-Deblurring

I personally used a technique (based on a paper from that list) that learns a kernel based on the idea that it's easier to learn a latent source image and a blur compared to learning a blurred image.

Could be some new hotness can get it done, but the real answer here is probably to record a video with a better angle, and closer. Maybe there's some way to hide the hardware so that it's not super obvious there's a camera.

Does this remove enough information so the original faces can not be reconstructed (eg.: with some de-blur magic)? The blur is different in every frame, I suspect more information can be reconstructed because of that. Maybe pixelization or filling with a single color is a better solution.

You could try some sort of Image restoration GAN to produce a modified output of the input image (which lies on the curved surface). I have conducted research on GANs for deblurring Fiducial markers, and simply put, the Network takes a blurred image containing fiducial markers and outputs a ‘deblurred image’ whose fiducial markers can be more easily detected. However, keep in mind that the GANs can perform a wide variety of operations on an input Image (other than just deblurring), hence, they are called Image restoration networks in general. What you could give a shot is, create a pipeline where the captured image goes into the GAN, then the output is fed to the Apriltag detector. Here is the link to our GitHub if you need a starter: https://github.com/York-SDCNLab/Ghost-DeblurGAN, and some of my other suggestions are NAFNet and HINet.