SOTAVerified

Super-Resolution

Super-Resolution is a task in computer vision that involves increasing the resolution of an image or video by generating missing high-frequency details from low-resolution input. The goal is to produce an output image with a higher resolution than the input image, while preserving the original content and structure.

( Credit: MemNet )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 3231–3240 of 3874 papers

TitleStatusHype
A Multi-Task Learning Model for Super Resolution of Wireless Channel Characteristics—0
Amortised MAP Inference for Image Super-resolution—0
A Modular Conditional Diffusion Framework for Image Reconstruction—0
A Mixed-Supervision Multilevel GAN Framework for Image Quality Enhancement—0
SREdgeNet: Edge Enhanced Single Image Super Resolution using Dense Edge Detection Network and Feature Merge Network—0
SRFeat: Single Image Super-Resolution with Feature Discrimination—0
A mathematical theory of super-resolution and two-point resolution—0
A mathematical theory of resolution limits for super-resolution of positive sources—0
SR-GAN for SR-gamma: super resolution of photon calorimeter images at collider experiments—0
Always Look on the Bright Side of the Field: Merging Pose and Contextual Data to Estimate Orientation of Soccer Players—0
Show:102550
← PrevPage 324 of 388Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified