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 351375 of 1589 papers

TitleStatusHype
Generative Adversarial Models for Extreme Geospatial Downscaling0
Trustworthy SR: Resolving Ambiguity in Image Super-resolution via Diffusion Models and Human Feedback0
See More Details: Efficient Image Super-Resolution by Experts MiningCode2
Deep 3D World Models for Multi-Image Super-Resolution Beyond Optical Flow0
You Only Need One Step: Fast Super-Resolution with Stable Diffusion via Scale Distillation0
Bridging the Domain Gap: A Simple Domain Matching Method for Reference-based Image Super-Resolution in Remote Sensing0
Vision-Informed Flow Image Super-Resolution with Quaternion Spatial Modeling and Dynamic Flow Convolution0
An efficient dual-branch framework via implicit self-texture enhancement for arbitrary-scale histopathology image super-resolution0
LKFormer: Large Kernel Transformer for Infrared Image Super-ResolutionCode1
Explaining the Implicit Neural Canvas: Connecting Pixels to Neurons by Tracing their Contributions0
Efficient Image Super-Resolution via Symmetric Visual Attention Network0
No-Clean-Reference Image Super-Resolution: Application to Electron Microscopy0
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
Image Processing GNN: Breaking Rigidity in Super-Resolution0
Learning Large-Factor EM Image Super-Resolution with Generative PriorsCode1
CoDe: An Explicit Content Decoupling Framework for Image Restoration0
CDFormer: When Degradation Prediction Embraces Diffusion Model for Blind Image Super-ResolutionCode2
Towards Progressive Multi-Frequency Representation for Image WarpingCode0
Learning Coupled Dictionaries from Unpaired Data for Image Super-Resolution0
Continuous Optical Zooming: A Benchmark for Arbitrary-Scale Image Super-Resolution in Real WorldCode1
Beyond Subspace Isolation: Many-to-Many Transformer for Light Field Image Super-resolutionCode0
Diffusion Models, Image Super-Resolution And Everything: A Survey0
Compressing Deep Image Super-resolution Models0
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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