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 76100 of 1589 papers

TitleStatusHype
SAM-DiffSR: Structure-Modulated Diffusion Model for Image Super-ResolutionCode2
See More Details: Efficient Image Super-Resolution by Experts MiningCode2
Transcending the Limit of Local Window: Advanced Super-Resolution Transformer with Adaptive Token DictionaryCode2
Transforming Image Super-Resolution: A ConvFormer-based Efficient ApproachCode2
CFAT: Unleashing Triangular Windows for Image Super-resolutionCode2
CDFormer: When Degradation Prediction Embraces Diffusion Model for Blind Image Super-ResolutionCode2
Improving the Stability and Efficiency of Diffusion Models for Content Consistent Super-ResolutionCode2
Neural Fields with Thermal Activations for Arbitrary-Scale Super-ResolutionCode2
Swift Parameter-free Attention Network for Efficient Super-ResolutionCode2
Diffusion Models for Image Restoration and Enhancement -- A Comprehensive SurveyCode2
Dual Aggregation Transformer for Image Super-ResolutionCode2
Efficient Mixed Transformer for Single Image Super-ResolutionCode2
Bicubic++: Slim, Slimmer, Slimmest -- Designing an Industry-Grade Super-Resolution NetworkCode2
Omni Aggregation Networks for Lightweight Image Super-ResolutionCode2
Implicit Diffusion Models for Continuous Super-ResolutionCode2
Learning Generative Structure Prior for Blind Text Image Super-resolutionCode2
SRFormerV2: Taking a Closer Look at Permuted Self-Attention for Image Super-ResolutionCode2
Efficient and Explicit Modelling of Image Hierarchies for Image RestorationCode2
Spatially-Adaptive Feature Modulation for Efficient Image Super-ResolutionCode2
Image Restoration with Mean-Reverting Stochastic Differential EquationsCode2
Image Super-Resolution using Efficient Striped Window TransformerCode2
Reference-based Image and Video Super-Resolution via C2-MatchingCode2
Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and RestorationCode2
Towards Lightweight Super-Resolution with Dual Regression LearningCode2
Decoupled-and-Coupled Networks: Self-Supervised Hyperspectral Image Super-Resolution with Subpixel FusionCode2
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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