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 1101–1125 of 1589 papers

TitleStatusHype
Robust Unpaired Single Image Super-Resolution of Faces—0
Rolling Shutter Super-Resolution—0
RRSR:Reciprocal Reference-based Image Super-Resolution with Progressive Feature Alignment and Selection—0
SAINT: Spatially Aware Interpolation NeTwork for Medical Slice Synthesis—0
SatDiffMoE: A Mixture of Estimation Method for Satellite Image Super-resolution with Latent Diffusion Models—0
Scale-aware Super-resolution Network with Dual Affinity Learning for Lesion Segmentation from Medical Images—0
Scene Text Image Super-Resolution via Content Perceptual Loss and Criss-Cross Transformer Blocks—0
SeCo-INR: Semantically Conditioned Implicit Neural Representations for Improved Medical Image Super-Resolution—0
Seeing Eye to AI? Applying Deep-Feature-Based Similarity Metrics to Information Visualization—0
SEGSRNet for Stereo-Endoscopic Image Super-Resolution and Surgical Instrument Segmentation—0
Self-FuseNet: Data Free Unsupervised Remote Sensing Image Super-Resolution—0
Selfie Periocular Verification using an Efficient Super-Resolution Approach—0
Self-Organized Residual Blocks for Image Super-Resolution—0
Self-Prior Guided Mamba-UNet Networks for Medical Image Super-Resolution—0
Self-Tuned Deep Super Resolution—0
Semantically Accurate Super-Resolution Generative Adversarial Networks—0
Semantic Encoder Guided Generative Adversarial Face Ultra-Resolution Network—0
Semantic Segmentation Prior for Diffusion-Based Real-World Super-Resolution—0
Semantic Segmentation Using Super Resolution Technique as Pre-Processing—0
Sequence Matters: Harnessing Video Models in 3D Super-Resolution—0
Seven ways to improve example-based single image super resolution—0
Sewer Image Super-Resolution with Depth Priors and Its Lightweight Network—0
ShipSRDet: An End-to-End Remote Sensing Ship Detector Using Super-Resolved Feature Representation—0
ShuffleUNet: Super resolution of diffusion-weighted MRIs using deep learning—0
Similarity-Aware Patchwork Assembly for Depth Image Super-Resolution—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
8SwinFIRPSNR29.36—Unverified
9CPAT+PSNR29.36—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