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Image Super-Resolution

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

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Fast and Accurate Image Super-Resolution with Deep Laplacian Pyramid Networks

Araxeus/PNG-Upscale • • 4 Oct 2017

However, existing methods often require a large number of network parameters and entail heavy computational loads at runtime for generating high-accuracy super-resolution results.

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"Zero-Shot" Super-Resolution using Deep Internal Learning

assafshocher/ZSSR • 17 Dec 2017

On such images, our method outperforms SotA CNN-based SR methods, as well as previous unsupervised SR methods.

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Recurrent Back-Projection Network for Video Super-Resolution

alterzero/RBPN-PyTorch • • CVPR 2019

We proposed a novel architecture for the problem of video super-resolution.

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Deep Back-Projection Networks for Single Image Super-resolution

alterzero/DBPN-Pytorch • • 4 Apr 2019

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

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SRFlow: Learning the Super-Resolution Space with Normalizing Flow

andreas128/SRFlow • • ECCV 2020

SRFlow therefore directly accounts for the ill-posed nature of the problem, and learns to predict diverse photo-realistic high-resolution images.

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A Fully Progressive Approach to Single-Image Super-Resolution

fperazzi/proSR • • 9 Apr 2018

Recent deep learning approaches to single image super-resolution have achieved impressive results in terms of traditional error measures and perceptual quality.

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A Comprehensive guide to Bayesian Convolutional Neural Network with Variational Inference

kumar-shridhar/PyTorch-BayesianCNN • • 8 Jan 2019

In this paper, Bayesian Convolutional Neural Network (BayesCNN) using Variational Inference is proposed, that introduces probability distribution over the weights.

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Pre-Trained Image Processing Transformer

huawei-noah/Pretrained-IPT • • CVPR 2021

To maximally excavate the capability of transformer, we present to utilize the well-known ImageNet benchmark for generating a large amount of corrupted image pairs.

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Revisiting RCAN: Improved Training for Image Super-Resolution

zudi-lin/rcan-it • • 27 Jan 2022

Image super-resolution (SR) is a fast-moving field with novel architectures attracting the spotlight.

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Multi-level Wavelet-CNN for Image Restoration

lpj0/MWCNN • 18 May 2018

With the modified U-Net architecture, wavelet transform is introduced to reduce the size of feature maps in the contracting subnetwork.

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