There are 14 repositories under srgan topic.
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.
A Novel Approach to Video Super-Resolution using Frame Recurrence and Generative Adversarial Networks | Python3 | PyTorch | OpenCV2 | GANs | CNNs
Generative Adversarial Network for single image super-resolution in high content screening microscopy images
An Unofficial PyTorch Implementation for Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
An implement of SRGAN(Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network) for tensorflow version
Tensorflow implementation of "Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network" (Ledig et al. 2017)
Generative Adversarial Networks with TensorFlow2, Keras and Python (Jupyter Notebooks Implementations)
Applied Self Supervised Learning techniques such as Jigsaw as pretext task, SRGAN and SimCLR for fine-grained classification
Photo Realistic Single Image Super-Resolution Using a Generative Adversarial Network implemented in Keras
SRGAN (super resolution generative adversarial networks) with WGAN loss function in TensorFlow
A tutorial to super resolution and SRGAN in PyTorch
A tensorflow-based implementation of SISR using EDSR, SRResNet, and SRGAN
PyTorch version of the paper: "Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network"
Generating super-resolution images using GANs
PyTorch implementation of the paper "Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network"