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 251275 of 1589 papers

TitleStatusHype
Learning Non-Local Spatial-Angular Correlation for Light Field Image Super-ResolutionCode1
Continuous Remote Sensing Image Super-Resolution based on Context Interaction in Implicit Function SpaceCode1
Hyperspectral Image Super Resolution with Real Unaligned RGB GuidanceCode1
Hypernetworks build Implicit Neural Representations of SoundsCode1
OSRT: Omnidirectional Image Super-Resolution with Distortion-aware TransformerCode1
Lightweight Image Super-Resolution with Superpixel Token InteractionCode1
Learning Correction Filter via Degradation-Adaptive Regression for Blind Single Image Super-ResolutionCode1
Equivalent Transformation and Dual Stream Network Construction for Mobile Image Super-ResolutionCode1
B-Spline Texture Coefficients Estimator for Screen Content Image Super-ResolutionCode1
Boosting Single Image Super-Resolution via Partial Channel ShiftingCode1
CutMIB: Boosting Light Field Super-Resolution via Multi-View Image BlendingCode1
Deep Random Projector: Accelerated Deep Image PriorCode1
Deep Arbitrary-Scale Image Super-Resolution via Scale-Equivariance PursuitCode1
Infrared Image Super-Resolution: Systematic Review, and Future TrendsCode1
Meta-Learned Kernel For Blind Super-Resolution Kernel EstimationCode1
U2Net: A General Framework with Spatial-Spectral-Integrated Double U-Net for Image FusionCode1
CiaoSR: Continuous Implicit Attention-in-Attention Network for Arbitrary-Scale Image Super-ResolutionCode1
Learning Detail-Structure Alternative Optimization for Blind Super-ResolutionCode1
Bridging Component Learning with Degradation Modelling for Blind Image Super-ResolutionCode1
Global Learnable Attention for Single Image Super-ResolutionCode1
Knowledge Distillation based Degradation Estimation for Blind Super-ResolutionCode1
From Coarse to Fine: Hierarchical Pixel Integration for Lightweight Image Super-ResolutionCode1
CHIMLE: Conditional Hierarchical IMLE for Multimodal Conditional Image SynthesisCode1
GAN Prior based Null-Space Learning for Consistent Super-ResolutionCode1
Perception-Oriented Single Image Super-Resolution using Optimal Objective 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
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