Popular GitHub Repositories for Srgan

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.

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

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

Awesome Generative Adversarial Networks with tensorflow

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

A modern PyTorch implementation of SRGAN

? 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)

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

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

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)

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

SRCNN_SRGAN_ESRGAN_ON_BRAIN_MRI

Research on ESRGAN (Enhanced Super-Resolution Generative Adversarial Networks, published in ECCV 2018), implemented in Tensorflow 2.0. All implementation deployed on Google Colab, and make some modify with origin Architecture include Losses.

Mozart - A Generative Art Platform

This is an Image Enhancement project which uses EDSR, WDSR, and SRGAN methods to increase the resolution of an image while also improving the details significantly.

This repository leverages Generative Adversarial Networks (GANs) to enhance image resolution for various applications, using the Super-Resolution GAN (SRGAN) architecture. The project includes a Jupyter Notebook for model training and a detailed research paper documenting the methodology and results.