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

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Showing 2541–2550 of 3874 papers

TitleStatusHype
Accurate super-resolution low-field brain MRI—0
Fast Online Video Super-Resolution with Deformable Attention Pyramid—0
An Optimal Transport Perspective on Unpaired Image Super-Resolution—0
CAESR: Conditional Autoencoder and Super-Resolution for Learned Spatial Scalability—0
Scale-arbitrary Invertible Image Downscaling—0
Deep Networks for Image and Video Super-Resolution—0
Image Superresolution using Scale-Recurrent Dense Network—0
End-to-End Optimization of Metasurfaces for Imaging with Compressed SensingCode0
Learning Multiple Probabilistic Degradation Generators for Unsupervised Real World Image Super ResolutionCode0
Revisiting L1 Loss in Super-Resolution: A Probabilistic View and Beyond—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified