Open Source Image and Video Restoration Toolbox for Super-resolution, Denoise, Deblurring, etc. Currently, it includes EDSR, RCAN, SRResNet, SRGAN, ESRGAN, EDVR, BasicVSR, SwinIR, ECBSR, etc. Also support StyleGAN2, DFDNet.

Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network

A collection of state-of-the-art video or single-image super-resolution architectures, reimplemented in tensorflow.

Tensorflow 2.x based implementation of EDSR, WDSR and SRGAN for single image super-resolution

A PyTorch implementation of SRGAN based on CVPR 2017 paper "Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network"

Tensorflow implementation of the SRGAN algorithm for single image super-resolution

Awesome Generative Adversarial Networks with tensorflow

collection of super-resolution models & algorithms

A Fast Deep Learning Model to Upsample Low Resolution Videos to High Resolution at 30fps

Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network | a PyTorch Tutorial to Super-Resolution

A modern PyTorch implementation of SRGAN

iSeeBetter: Spatio-Temporal Video Super Resolution using Recurrent-Generative Back-Projection Networks | Python3 | PyTorch | GANs | CNNs | ResNets | RNNs | Published in Springer Journal of Computational Visual Media, September 2020, Tsinghua University Press

Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network implemented in Keras

Image Super-Resolution Using SRCNN, DRRN, SRGAN, CGAN in Pytorch

Applying SRGAN technique implemented in https://github.com/zsdonghao/SRGAN on videos to super resolve them.

Official PyTorch implementation of Harmonizing Maximum Likelihood with GANs for Multimodal Conditional Generation (ICLR 2019)

A PyTorch implementation of SRGAN specific for Anime Super Resolution based on "Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network". And another PyTorch WGAN-gp implementation of SRGAN referring to "Improved Training of Wasserstein GANs".

🔥 Real-time Super Resolution enhancement (4x) with content loss and relativistic adversarial optimization 🔥

Various models for handling underexposure, overexposure, super-resolution, shadow removal, etc.

Various GAN Model