Image Super-Resolution

390 papers with code • 53 benchmarks • 30 datasets

In this task, we try to upsample the image and create a high-resolution image with help of a low-resolution image. The behavior of optimization-based super-resolution methods is principally driven by the choice of the objective function. Recent work has largely focused on minimizing the mean squared reconstruction error. The resulting estimates have high peak signal-to-noise ratios, but they are often lacking high-frequency details and are perceptually unsatisfying in the sense that they fail to match the fidelity expected at the higher resolution.

Libraries

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.

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).

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.

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).

Deep Back-Projection Networks For Super-Resolution

thstkdgus35/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.

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.