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 2141–2150 of 3874 papers

TitleStatusHype
TR-MISR: Multiimage Super-Resolution Based on Feature Fusion With TransformersCode1
Fast Online Video Super-Resolution with Deformable Attention Pyramid—0
An Optimal Transport Perspective on Unpaired Image Super-Resolution—0
Gradient Variance Loss for Structure-Enhanced Image Super-ResolutionCode1
CAESR: Conditional Autoencoder and Super-Resolution for Learned Spatial Scalability—0
Proximal Denoiser for Convergent Plug-and-Play Optimization with Nonconvex RegularizationCode1
Scale-arbitrary Invertible Image Downscaling—0
End-to-End Optimization of Metasurfaces for Imaging with Compressed SensingCode0
Image Superresolution using Scale-Recurrent Dense Network—0
Deep Networks for Image and Video Super-Resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified