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 101125 of 1589 papers

TitleStatusHype
SAM-DiffSR: Structure-Modulated Diffusion Model for Image Super-ResolutionCode2
AnySR: Realizing Image Super-Resolution as Any-Scale, Any-ResourceCode2
CDFormer:When Degradation Prediction Embraces Diffusion Model for Blind Image Super-ResolutionCode2
Spatially-Adaptive Feature Modulation for Efficient Image Super-ResolutionCode2
SSL: A Self-similarity Loss for Improving Generative Image Super-resolutionCode2
Swift Parameter-free Attention Network for Efficient Super-ResolutionCode2
Geodesic Diffusion Models for Medical Image-to-Image GenerationCode2
Progressive Focused Transformer for Single Image Super-ResolutionCode2
A-ESRGAN: Training Real-World Blind Super-Resolution with Attention U-Net DiscriminatorsCode1
Combining Attention Module and Pixel Shuffle for License Plate Super-ResolutionCode1
Deep learning architectural designs for super-resolution of noisy imagesCode1
Component Divide-and-Conquer for Real-World Image Super-ResolutionCode1
A Benchmark for Chinese-English Scene Text Image Super-resolutionCode1
Deep Interleaved Network for Image Super-Resolution With Asymmetric Co-AttentionCode1
Deep Learning-Based CKM Construction with Image Super-ResolutionCode1
A Spectral Diffusion Prior for Hyperspectral Image Super-ResolutionCode1
CiaoSR: Continuous Implicit Attention-in-Attention Network for Arbitrary-Scale Image Super-ResolutionCode1
ControlSR: Taming Diffusion Models for Consistent Real-World Image Super ResolutionCode1
Deep Burst Super-ResolutionCode1
A Dynamic Residual Self-Attention Network for Lightweight Single Image Super-ResolutionCode1
Deep Cyclic Generative Adversarial Residual Convolutional Networks for Real Image Super-ResolutionCode1
Collapsible Linear Blocks for Super-Efficient Super ResolutionCode1
AeroRIT: A New Scene for Hyperspectral Image AnalysisCode1
Activating Wider Areas in Image Super-ResolutionCode1
Closed-loop Matters: Dual Regression Networks for Single Image Super-ResolutionCode1
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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