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

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

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

pytorch implementation for Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network arXiv:1609.04802

An implementation of SRGAN model in Keras

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

Torch Implementation of "Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network"

Context Encoding for Semantic Segmentation MegaDepth: Learning Single-View Depth Prediction from Internet Photos LiteFlowNet: A Lightweight Convolutional Neural Network for Optical Flow Estimation PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume On the Robustness of Semantic Segmentation Models to Adversarial Attacks SPLATNet: Sparse Lattice Networks for Point Cloud Processing Left-Right Comparative Recurrent Model for Stereo Matching Enhancing the Spatial Resolution of Stereo Images using a Parallax Prior Unsupervised CCA Discovering Point Lights with Intensity Distance Fields CBMV: A Coalesced Bidirectional Matching Volume for Disparity Estimation Learning a Discriminative Feature Network for Semantic Segmentation Revisiting Dilated Convolution: A Simple Approach for Weakly- and Semi- Supervised Semantic Segmentation Unsupervised Deep Generative Adversarial Hashing Network Monocular Relative Depth Perception with Web Stereo Data Supervision Single Image Reflection Separation with Perceptual Losses Zoom and Learn: Generalizing Deep Stereo Matching to Novel Domains EPINET: A Fully-Convolutional Neural Network for Light Field Depth Estimation by Using Epipolar Geometry FoldingNet: Interpretable Unsupervised Learning on 3D Point Clouds Decorrelated Batch Normalization Unsupervised Learning of Depth and Egomotion from Monocular Video Using 3D Geometric Constraints PU-Net: Point Cloud Upsampling Network Real-Time Monocular Depth Estimation using Synthetic Data with Domain Adaptation via Image Style Transfer Tell Me Where To Look: Guided Attention Inference Network Residual Dense Network for Image Super-Resolution Reflection Removal for Large-Scale 3D Point Clouds PlaneNet: Piece-wise Planar Reconstruction from a Single RGB Image Fully Convolutional Adaptation Networks for Semantic Segmentation CRRN: Multi-Scale Guided Concurrent Reflection Removal Network DenseASPP: Densely Connected Networks for Semantic Segmentation SGAN: An Alternative Training of Generative Adversarial Networks Multi-Agent Diverse Generative Adversarial Networks Robust Depth Estimation from Auto Bracketed Images AdaDepth: Unsupervised Content Congruent Adaptation for Depth Estimation DeepMVS: Learning Multi-View Stereopsis GeoNet: Unsupervised Learning of Dense Depth, Optical Flow and Camera Pose GeoNet: Geometric Neural Network for Joint Depth and Surface Normal Estimation Single-Image Depth Estimation Based on Fourier Domain Analysis Single View Stereo Matching Pyramid Stereo Matching Network A Unifying Contrast Maximization Framework for Event Cameras, with Applications to Motion, Depth, and Optical Flow Estimation Image Correction via Deep Reciprocating HDR Transformation Occlusion Aware Unsupervised Learning of Optical Flow PAD-Net: Multi-Tasks Guided Prediciton-and-Distillation Network for Simultaneous Depth Estimation and Scene Parsing Surface Networks Structured Attention Guided Convolutional Neural Fields for Monocular Depth Estimation TextureGAN: Controlling Deep Image Synthesis with Texture Patches Aperture Supervision for Monocular Depth Estimation Two-Stream Convolutional Networks for Dynamic Texture Synthesis Unsupervised Learning of Single View Depth Estimation and Visual Odometry with Deep Feature Reconstruction Left/Right Asymmetric Layer Skippable Networks Learning to See in the Dark

3D super-resolution using Generative Adversarial Networks

Unofficial Implementation of "Unsupervised Image Super-Resolution using Cycle-in-Cycle Generative Adversarial Networks" in CVPR 2018.

The Super-Resolution for Renewable Resource Data (sup3r) software uses generative adversarial networks to create synthetic high-resolution wind and solar spatiotemporal data from coarse low-resolution inputs.

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".

Implementation of [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network arXiv:1609.04802v2]

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

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

Official implementation of "AEROMamba: An efficient architecture for audio super-resolution using generative adversarial networks and state space models", presented in LAMIR 2024 Workshop

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

SRGAN (Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network) implementation using PyTorch framework

Pytorch implementation of "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)

Developed VisionSoC, an advanced image upscaling model using Enhanced Super Resolution Generative Adversarial Networks (ESRGAN) with Python, leveraging frameworks such as TensorFlow and Keras. Created a comprehensive web-based application for the model using HTML, CSS, and JavaScript, and integrated the frontend with the backend using Flask.

PyTorch implementation of "MedSRGAN: medical images super-resolution using generative adversarial networks"

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

Super Resolution Generative Adversarial Network (SRGAN) using Tensorflow 2.0

Pytorch implementation of "Unsupervised Image Super-Resolution using Cycle-in-Cycle Generative Adversarial Networks", CVPRW 2018

Super-Resolution Generative Adversarial Networks (SRGAN) is a deep learning application to generate high resolution (HR) images from low resolution (LR) image. In this work, we use SRGAN to up-scale 32x32 images to 128x128 pixels. Meanwhile, we evaluate the impact of different camera parameters on the quality of final up-scaled (high resolution) images and infer from these stimuli to understand what the network is able to learn.

Generative Adversarial Networks are used to super resolve turbulent flow fields from low resolution (RANS/LES) fields to high resolution (DNS) fields without solving NS equations numerically.

This repository includes code of training/testing of our work published in NTIRE-2020 workshop titled "Unsupervised Single Image Super-Resolution Network (USISResNet) for Real-World Data Using Generative Adversarial Network".

A pytorch implementation of "Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network"