Ghost-DeblurGAN VS DeblurGANv2

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.

I am trying to implement an image deblurring model (DeblurGANv2) The generator takes blurred images as an input and outputs the corrected image, the discriminator helps in training the generator. The model has been trained and I have run and deblurred images on a GPU. I am completely new to the domain of Machine learning and I am trying to implement this on an FPGA. I wanted your guys help on how to do this. The .h5 files contains the weights of the NN, how do I figure out its structure and put it on my FPGA. Can you please suggest some tools that might help or github links for people who have implemented similar stuff.