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 1326–1350 of 1589 papers

TitleStatusHype
Benefiting from Bicubically Down-Sampled Images for Learning Real-World Image Super-Resolution—0
Single MR Image Super-Resolution via Channel Splitting and Serial Fusion Network—0
Single-photon Image Super-resolution via Self-supervised Learning—0
Single-sample image-fusion upsampling of fluorescence lifetime images—0
Single-Step Latent Consistency Model for Remote Sensing Image Super-Resolution—0
ACDMSR: Accelerated Conditional Diffusion Models for Single Image Super-Resolution—0
Benefiting from Multitask Learning to Improve Single Image Super-Resolution—0
Benchmarking Ultra-High-Definition Image Super-Resolution—0
Soft-IntroVAE for Continuous Latent space Image Super-Resolution—0
Solving Diffusion ODEs with Optimal Boundary Conditions for Better Image Super-Resolution—0
Solving Video Inverse Problems Using Image Diffusion Models—0
Bayesian Sparse Representation for Hyperspectral Image Super Resolution—0
SOUP-GAN: Super-Resolution MRI Using Generative Adversarial Networks—0
BadSR: Stealthy Label Backdoor Attacks on Image Super-Resolution—0
Sparse-based Domain Adaptation Network for OCTA Image Super-Resolution Reconstruction—0
Sparse Coding Approach for Multi-Frame Image Super Resolution—0
Sparsity-based Color Image Super Resolution via Exploiting Cross Channel Constraints—0
Backdoor Attacks against Image-to-Image Networks—0
A Wavelet Diffusion GAN for Image Super-Resolution—0
A Unified Framework to Super-Resolve Face Images of Varied Low Resolutions—0
Accurate and Lightweight Image Super-Resolution with Model-Guided Deep Unfolding Network—0
VarSR: Variational Super-Resolution Network for Very Low Resolution Images—0
Spatial-Spectral Residual Network for Hyperspectral Image Super-Resolution—0
Augmented Equivariant Attention Networks for Microscopy Image Reconstruction—0
A two-stage 3D Unet framework for multi-class segmentation on full resolution image—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