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 601625 of 1589 papers

TitleStatusHype
Deep Residual Axial Networks0
Fast Full-Resolution Target-Adaptive CNN-Based Pansharpening FrameworkCode0
DLGSANet: Lightweight Dynamic Local and Global Self-Attention Networks for Image Super-Resolution0
Learning Correction Filter via Degradation-Adaptive Regression for Blind Single Image Super-ResolutionCode1
Boosting Single Image Super-Resolution via Partial Channel ShiftingCode1
MSRA-SR: Image Super-resolution Transformer with Multi-scale Shared Representation Acquisition0
Lightweight Image Super-Resolution with Superpixel Token InteractionCode1
HSR-Diff: Hyperspectral Image Super-Resolution via Conditional Diffusion Models0
Content-Aware Local GAN for Photo-Realistic Super-Resolution0
Toward Accurate Post-Training Quantization for Image Super ResolutionCode0
Spectral Bayesian Uncertainty for Image Super-Resolution0
Deep Random Projector: Accelerated Deep Image PriorCode1
Deep Arbitrary-Scale Image Super-Resolution via Scale-Equivariance PursuitCode1
CutMIB: Boosting Light Field Super-Resolution via Multi-View Image BlendingCode1
Cross-Guided Optimization of Radiance Fields With Multi-View Image Super-Resolution for High-Resolution Novel View Synthesis0
B-Spline Texture Coefficients Estimator for Screen Content Image Super-ResolutionCode1
Rethinking Image Super Resolution From Long-Tailed Distribution Learning Perspective0
Equivalent Transformation and Dual Stream Network Construction for Mobile Image Super-ResolutionCode1
Efficient Image Super-Resolution with Feature Interaction Weighted Hybrid NetworkCode0
Single-Image Super-Resolution Reconstruction based on the Differences of Neighboring Pixels0
Transformer and GAN Based Super-Resolution Reconstruction Network for Medical Images0
Infrared Image Super-Resolution: Systematic Review, and Future TrendsCode1
Multi-Reference Image Super-Resolution: A Posterior Fusion Approach0
Reference-based Image and Video Super-Resolution via C2-MatchingCode2
Meta-Learned Kernel For Blind Super-Resolution Kernel EstimationCode1
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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