SOTAVerified

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.

Papers

Showing 13011325 of 1589 papers

TitleStatusHype
Suppressing Model Overfitting for Image Super-Resolution Networks0
Hybrid Function Sparse Representation towards Image Super ResolutionCode0
Generative Adversarial Networks in Computer Vision: A Survey and TaxonomyCode1
Second-Order Attention Network for Single Image Super-ResolutionCode0
Hyperspectral Image Super-Resolution With Optimized RGB GuidanceCode0
Residual Networks for Light Field Image Super-Resolution0
ODE-Inspired Network Design for Single Image Super-Resolution0
Towards Real Scene Super-Resolution with Raw ImagesCode1
Medical image super-resolution method based on dense blended attention network0
Ensemble Super-Resolution with A Reference DatasetCode0
Adapting Image Super-Resolution State-of-the-arts and Learning Multi-model Ensemble for Video Super-Resolution0
SinGAN: Learning a Generative Model from a Single Natural ImageCode1
Understanding Opportunities for Efficiency in Single-image Super Resolution Networks0
Unsupervised and Unregistered Hyperspectral Image Super-Resolution with Mutual Dirichlet-NetCode0
Super-Resolved Image Perceptual Quality Improvement via Multi-Feature Discriminators0
Multi-scale deep neural networks for real image super-resolutionCode0
Adaptive Transform Domain Image Super-resolution Via Orthogonally Regularized Deep Networks0
Modulating Image Restoration with Continual Levels via Adaptive Feature Modification LayersCode0
Process of image super-resolution0
A Deep Journey into Super-resolution: A surveyCode0
Super Resolution Convolutional Neural Network Models for Enhancing Resolution of Rock Micro-CT Images0
MAANet: Multi-view Aware Attention Networks for Image Super-ResolutionCode0
Evaluating Robustness of Deep Image Super-Resolution against Adversarial AttacksCode0
Difficulty-aware Image Super Resolution via Deep Adaptive Dual-NetworkCode0
Blind Super-Resolution With Iterative Kernel CorrectionCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DRCT-LPSNR29.54Unverified
2HMA†PSNR29.51Unverified
3Hi-IR-LPSNR29.49Unverified
4HAT-LPSNR29.47Unverified
5HAT_FIRPSNR29.44Unverified
6DRCTPSNR29.4Unverified
7HATPSNR29.38Unverified
8SwinFIRPSNR29.36Unverified
9CPAT+PSNR29.36Unverified
10CPATPSNR29.34Unverified
#ModelMetricClaimedVerifiedStatus
1DRCT-LPSNR28.16Unverified
2HMA†PSNR28.13Unverified
3Hi-IR-LPSNR28.13Unverified
4HAT-LPSNR28.09Unverified
5HAT_FIRPSNR28.07Unverified
6CPAT+PSNR28.06Unverified
7DRCTPSNR28.06Unverified
8HATPSNR28.05Unverified
9CPATPSNR28.04Unverified
10SwinFIRPSNR28.03Unverified
#ModelMetricClaimedVerifiedStatus
1Hi-IR-LPSNR28.72Unverified
2DRCT-LPSNR28.7Unverified
3HMA†PSNR28.69Unverified
4HAT-LPSNR28.6Unverified
5HAT_FIRPSNR28.43Unverified
6DRCTPSNR28.4Unverified
7HATPSNR28.37Unverified
8CPAT+PSNR28.33Unverified
9CPATPSNR28.22Unverified
10PFTPSNR28.2Unverified