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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 751–775 of 1589 papers

TitleStatusHype
Deep unfolding Network for Hyperspectral Image Super-Resolution with Automatic Exposure Correction—0
Learning Correction Errors via Frequency-Self Attention for Blind Image Super-Resolution—0
Adaptive Multi-modal Fusion of Spatially Variant Kernel Refinement with Diffusion Model for Blind Image Super-Resolution—0
Low-Res Leads the Way: Improving Generalization for Super-Resolution by Self-Supervised Learning—0
Text-guided Explorable Image Super-resolution—0
Navigating Beyond Dropout: An Intriguing Solution Towards Generalizable Image Super Resolution—0
Generative AI in Vision: A Survey on Models, Metrics and Applications—0
Generative Adversarial Models for Extreme Geospatial Downscaling—0
Trustworthy SR: Resolving Ambiguity in Image Super-resolution via Diffusion Models and Human Feedback—0
You Only Need One Step: Fast Super-Resolution with Stable Diffusion via Scale Distillation—0
Deep 3D World Models for Multi-Image Super-Resolution Beyond Optical Flow—0
Vision-Informed Flow Image Super-Resolution with Quaternion Spatial Modeling and Dynamic Flow Convolution—0
Bridging the Domain Gap: A Simple Domain Matching Method for Reference-based Image Super-Resolution in Remote Sensing—0
An efficient dual-branch framework via implicit self-texture enhancement for arbitrary-scale histopathology image super-resolution—0
Explaining the Implicit Neural Canvas: Connecting Pixels to Neurons by Tracing their Contributions—0
Efficient Image Super-Resolution via Symmetric Visual Attention Network—0
No-Clean-Reference Image Super-Resolution: Application to Electron Microscopy—0
CoDe: An Explicit Content Decoupling Framework for Image Restoration—0
Image Processing GNN: Breaking Rigidity in Super-Resolution—0
Towards Progressive Multi-Frequency Representation for Image WarpingCode0
Beyond Subspace Isolation: Many-to-Many Transformer for Light Field Image Super-resolutionCode0
Learning Coupled Dictionaries from Unpaired Data for Image Super-Resolution—0
Diffusion Models, Image Super-Resolution And Everything: A Survey—0
Compressing Deep Image Super-resolution Models—0
Image Super-resolution Reconstruction Network based on Enhanced Swin Transformer via Alternating Aggregation of Local-Global Features—0
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All datasetsSet14 - 4x upscalingBSD100 - 4x upscalingUrban100 - 4x upscalingManga109 - 4x upscalingSet5 - 2x upscalingSet14 - 2x upscalingSet5 - 3x upscalingBSD100 - 2x upscalingUrban100 - 2x upscalingSet14 - 3x upscalingUrban100 - 3x upscalingBSD100 - 3x upscaling

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DRCT-LPSNR29.54—Unverified
2HMA†PSNR29.51—Unverified
3Hi-IR-LPSNR29.49—Unverified
4HAT-LPSNR29.47—Unverified
5HAT_FIRPSNR29.44—Unverified
6DRCTPSNR29.4—Unverified
7HATPSNR29.38—Unverified
8CPAT+PSNR29.36—Unverified
9SwinFIRPSNR29.36—Unverified
10CPATPSNR29.34—Unverified
#ModelMetricClaimedVerifiedStatus
1DRCT-LPSNR28.16—Unverified
2HMA†PSNR28.13—Unverified
3Hi-IR-LPSNR28.13—Unverified
4HAT-LPSNR28.09—Unverified
5HAT_FIRPSNR28.07—Unverified
6CPAT+PSNR28.06—Unverified
7DRCTPSNR28.06—Unverified
8HATPSNR28.05—Unverified
9CPATPSNR28.04—Unverified
10SwinFIRPSNR28.03—Unverified
#ModelMetricClaimedVerifiedStatus
1Hi-IR-LPSNR28.72—Unverified
2DRCT-LPSNR28.7—Unverified
3HMA†PSNR28.69—Unverified
4HAT-LPSNR28.6—Unverified
5HAT_FIRPSNR28.43—Unverified
6DRCTPSNR28.4—Unverified
7HATPSNR28.37—Unverified
8CPAT+PSNR28.33—Unverified
9CPATPSNR28.22—Unverified
10PFTPSNR28.2—Unverified