Computer Vision
 
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Image Super-Resolution

610 papers with code • 61 benchmarks • 39 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.

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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.

138
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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.

80
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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.

60
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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).

47
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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).

45
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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.

44
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Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network

alexjc/neural-enhance • CVPR 2016

This means that the super-resolution (SR) operation is performed in HR space.

39
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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.

20
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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).

20
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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.

17
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