167 Open source projects that are alternatives of or similar to Deeply Recursive Cnn Tf

Fast and Accurate One-Stage Space-Time Video Super-Resolution (accepted in CVPR 2020)

Image Super-Resolution for Anime-Style Art

Hyperspectral Image Spatial Super-Resolution via 3D-Full-Convolutional-Neural-Network

Image Restoration Toolbox (PyTorch). Training and testing codes for USRNet, DnCNN, FFDNet, SRMD, DPSR, ESRGAN

Tensorflow implementation of Enhanced Deep Residual Networks for Single Image Super-Resolution

TensorFlow implementation of the Efficient Sub-Pixel Convolutional Neural Network

Image Super-Resolution Using Deep Convolutional Networks in Tensorflow https://arxiv.org/abs/1501.00092v3

The py version of toflow → https://github.com/anchen1011/toflow

Tensorflow implementation of pixel-recursive-super-resolution(Google Brain paper: https://arxiv.org/abs/1702.00783)

Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising (TIP, 2017)

Benchmark and resources for single super-resolution algorithms

A modern PyTorch implementation of SRGAN

ESRGAN (Enhanced Super-Resolution Generative Adversarial Networks, published in ECCV 2018) implemented in Tensorflow 2.0+. This is an unofficial implementation. With Colab.

A tensorflow implementation of "Fast and Accurate Image Super Resolution by Deep CNN with Skip Connection and Network in Network", a deep learning based Single-Image Super-Resolution (SISR) model.

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

Official SRFlow training code: Super-Resolution using Normalizing Flow in PyTorch

Image Super-Resolution Using Very Deep Residual Channel Attention Networks Implementation in Tensorflow

Learning Continuous Image Representation with Local Implicit Image Function, in CVPR 2021 (Oral)

Caffe implementation of "Fast and Accurate Single Image Super-Resolution via Information Distillation Network" (CVPR 2018)

Torch code for our CVPR 2018 paper "Residual Dense Network for Image Super-Resolution" (Spotlight)

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

Learning a Single Convolutional Super-Resolution Network for Multiple Degradations (CVPR, 2018) (Matlab)

Super Resolution of picture images using deep learning

TOFlow: Video Enhancement with Task-Oriented Flow

Scale-wise Convolution for Image Restoration

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

Pytorch implementation of Structure-Preserving Super Resolution with Gradient Guidance (CVPR 2020)

Matlab reimplementation of SCNSR

Deep Plug-and-Play Super-Resolution for Arbitrary Blur Kernels (CVPR, 2019) (PyTorch)

Latest development of ISR/VSR. Papers and related resources, mainly state-of-the-art and novel works in ICCV, ECCV and CVPR about image super-resolution and video super-resolution.

A PyTorch implementation of SRGAN based on CVPR 2017 paper "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.

Code of our winning entry to NTIRE super-resolution challenge, CVPR 2018

Natural and Realistic Single Image Super-Resolution with Explicit Natural Manifold Discrimination (CVPR, 2019)

Torch implementation of "Enhanced Deep Residual Networks for Single Image Super-Resolution"

Code repo for "Deep Generative Adversarial Residual Convolutional Networks for Real-World Super-Resolution" (CVPRW NTIRE2020).

Deep Unfolding Network for Image Super-Resolution (CVPR, 2020) (PyTorch)

Awesome Resources for Advanced Computer Vision Topics

A Python implementation of RAISR

Official repository of JSI-GAN (Accepted at AAAI 2020).

The project is an official implement of our CVPR2018 paper "Deep Back-Projection Networks for Super-Resolution" (Winner of NTIRE2018 and PIRM2018)

Test Time Augmentation (TTA) wrapper for computer vision tasks: segmentation, classification, super-resolution, ... etc.

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

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

Video, Image and GIF upscale/enlarge(Super-Resolution) and Video frame interpolation. Achieved with Waifu2x, Real-ESRGAN, SRMD, RealSR, Anime4K, RIFE, CAIN, DAIN, and ACNet.

Coarse-to-Fine CNN for Image Super-Resolution (IEEE Transactions on Multimedia,2020)

Real-World Super-Resolution via Kernel Estimation and Noise Injection

Implementation of 'Image Super-Resolution using Deep Convolutional Network'

Closed-loop Matters: Dual Regression Networks for Single Image Super-Resolution

It is a re-implementation of paper named "Deep Video Super-Resolution Network Using Dynamic Upsampling Filters Without Explicit Motion Compensation" called VSR-DUF model. There are both training codes and test codes about VSR-DUF based tensorflow.

VDSR (CVPR2016) pytorch implementation

Trainable models and NN optimization tools

All image quality metrics you need in one package.

torch implementation of srgan

A Pytorch implemented Deep SR-ITM (ICCV2019)

A Collection of Papers and Codes for ECCV2020 Low Level Vision or Image Reconstruction

Super-resolution (SR) is a method of creating images with higher resolution from a set of low resolution images.

3D super-resolution using Generative Adversarial Networks

Official implementation: Intermediate Layer Optimization for Inverse Problems using Deep Generative Models

Real-time video quality improvement for applications such as video-chat using Perceptual Losses