Image Super-Resolution

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.

Benchmarks

Libraries

Datasets

Subtasks

Most implemented papers

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

Content

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

Image Super-Resolution Using Deep Convolutional Networks

Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network

Enhanced Deep Residual Networks for Single Image Super-Resolution

ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks

SinGAN: Learning a Generative Model From a Single Natural Image

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

Image Super-Resolution Using Very Deep Residual Channel Attention Networks

Residual Dense Network for Image Super-Resolution