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 11761200 of 1589 papers

TitleStatusHype
An Optimal Transport Perspective on Unpaired Image Super-Resolution0
Context Reasoning Attention Network for Image Super-Resolution0
Content-decoupled Contrastive Learning-based Implicit Degradation Modeling for Blind Image Super-Resolution0
Content-Aware Local GAN for Photo-Realistic Super-Resolution0
Content-adaptive Representation Learning for Fast Image Super-resolution0
Real-Time Super-Resolution for Real-World Images on Mobile Devices0
ConsisSR: Delving Deep into Consistency in Diffusion-based Image Super-Resolution0
Con-Patch: When a Patch Meets its Context0
Real-World Image Super Resolution via Unsupervised Bi-directional Cycle Domain Transfer Learning based Generative Adversarial Network0
Real-World Single Image Super-Resolution Under Rainy Condition0
Conditioned Regression Models for Non-Blind Single Image Super-Resolution0
Real-World Super-Resolution of Face-Images from Surveillance Cameras0
Compressing Deep Image Super-resolution Models0
Real-World Video for Zoom Enhancement based on Spatio-Temporal Coupling0
Recognition-Guided Diffusion Model for Scene Text Image Super-Resolution0
Compound Attention and Neighbor Matching Network for Multi-contrast MRI Super-resolution0
Combined Channel and Spatial Attention-based Stereo Endoscopic Image Super-Resolution0
Suppressing Model Overfitting for Image Super-Resolution Networks0
Recurrent Super-Resolution Method for Enhancing Low Quality Thermal Facial Data0
CoDe: An Explicit Content Decoupling Framework for Image Restoration0
CMISR: Circular Medical Image Super-Resolution0
CLIP-SR: Collaborative Linguistic and Image Processing for Super-Resolution0
Unpaired MRI Super Resolution with Contrastive Learning0
Reference-Conditioned Super-Resolution by Neural Texture Transfer0
CLIP-aware Domain-Adaptive Super-Resolution0
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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