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 801825 of 1589 papers

TitleStatusHype
Conditional Simulation Using Diffusion Schrödinger BridgesCode1
Real-World Blind Super-Resolution via Feature Matching with Implicit High-Resolution PriorsCode2
Multi-image Super-resolution via Quality Map Associated Attention Network0
Convolutional Neural Network Modelling for MODIS Land Surface Temperature Super-ResolutionCode1
Single Image Super-Resolution Methods: A Survey0
Deep Constrained Least Squares for Blind Image Super-ResolutionCode2
Memory-augmented Deep Unfolding Network for Guided Image Super-resolution0
Trained Model in Supervised Deep Learning is a Conditional Risk MinimizerCode0
Patch-Based Stochastic Attention for Image EditingCode0
An Optimal Transport Perspective on Unpaired Image Super-Resolution0
Gradient Variance Loss for Structure-Enhanced Image Super-ResolutionCode1
Deep Networks for Image and Video Super-Resolution0
Image Superresolution using Scale-Recurrent Dense Network0
Revisiting RCAN: Improved Training for Image Super-ResolutionCode1
Learning Multiple Probabilistic Degradation Generators for Unsupervised Real World Image Super ResolutionCode0
Hyperspectral Image Super-resolution with Deep Priors and Degradation Model InversionCode1
A Review of Deep Learning Based Image Super-resolution Techniques0
Robust Unpaired Single Image Super-Resolution of Faces0
Improving Clinical Diagnosis Performance with Automated X-ray Scan Quality Enhancement Algorithms0
Dual Perceptual Loss for Single Image Super-Resolution Using ESRGAN0
CISRNet: Compressed Image Super-Resolution NetworkCode0
SDT-DCSCN for Simultaneous Super-Resolution and Deblurring of Text ImagesCode0
Flexible Style Image Super-Resolution using Conditional ObjectiveCode1
Efficient Non-Local Contrastive Attention for Image Super-ResolutionCode1
Uncovering the Over-smoothing Challenge in Image Super-Resolution: Entropy-based Quantification and Contrastive Optimization0
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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
8CPAT+PSNR29.36Unverified
9SwinFIRPSNR29.36Unverified
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