Image Super-Resolution

539 papers with code • 60 benchmarks • 38 datasets

Image Super-Resolution is a machine learning task where the goal is to increase the resolution of an image, often by a factor of 4x or more, while maintaining its content and details as much as possible. The end result is a high-resolution version of the original image. This task can be used for various applications such as improving image quality, enhancing visual detail, and increasing the accuracy of computer vision algorithms.


Use these libraries to find Image Super-Resolution models and implementations

Most implemented papers

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

tensorflow/models CVPR 2017

The adversarial loss pushes our solution to the natural image manifold using a discriminator network that is trained to differentiate between the super-resolved images and original photo-realistic images.

Perceptual Losses for Real-Time Style Transfer and Super-Resolution

alexjc/neural-enhance 27 Mar 2016

We consider image transformation problems, where an input image is transformed into an output image.

Image Super-Resolution Using Deep Convolutional Networks

nagadomi/waifu2x 31 Dec 2014

We further show that traditional sparse-coding-based SR methods can also be viewed as a deep convolutional network.

SinGAN: Learning a Generative Model from a Single Natural Image

tamarott/SinGAN ICCV 2019

We introduce SinGAN, an unconditional generative model that can be learned from a single natural image.

Enhanced Deep Residual Networks for Single Image Super-Resolution

LimBee/NTIRE2017 10 Jul 2017

Recent research on super-resolution has progressed with the development of deep convolutional neural networks (DCNN).

ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks

xinntao/ESRGAN 1 Sep 2018

To further enhance the visual quality, we thoroughly study three key components of SRGAN - network architecture, adversarial loss and perceptual loss, and improve each of them to derive an Enhanced SRGAN (ESRGAN).

Image Super-Resolution Using Very Deep Residual Channel Attention Networks

yulunzhang/RCAN ECCV 2018

To solve these problems, we propose the very deep residual channel attention networks (RCAN).

Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising

cszn/DnCNN 13 Aug 2016

Discriminative model learning for image denoising has been recently attracting considerable attentions due to its favorable denoising performance.

Deep Back-Projection Networks For Super-Resolution

sanghyun-son/EDSR-PyTorch CVPR 2018

The feed-forward architectures of recently proposed deep super-resolution networks learn representations of low-resolution inputs, and the non-linear mapping from those to high-resolution output.