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

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

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

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

A modern PyTorch implementation of SRGAN

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

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

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

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

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".

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

A Novel Approach to Video Super-Resolution using Frame Recurrence and Generative Adversarial Networks | Python3 | PyTorch | OpenCV2 | GANs | CNNs

Various GAN Model

An Unofficial PyTorch Implementation for Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network

A Caffe Implementation of SRGAN

Generative Adversarial Network for single image super-resolution in high content screening microscopy images

An implement of SRGAN(Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network) for tensorflow version

This Repository Contains Solution to the Assignments of the Generative Adversarial Networks (GANs) Specialization from deeplearning.ai on Coursera Taught by Sharon Zhou, Eda Zhou, Eric Zelikman

Tensorflow implementation of "Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network" (Ledig et al. 2017)

Applied Self Supervised Learning techniques such as Jigsaw as pretext task, SRGAN and SimCLR for fine-grained classification

Generative Adversarial Networks with TensorFlow2, Keras and Python (Jupyter Notebooks Implementations)

SRGAN (super resolution generative adversarial networks) with WGAN loss function in TensorFlow