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 3591–3600 of 3874 papers

TitleStatusHype
Learning Discriminative Multilevel Structured Dictionaries for Supervised Image Classification—0
Frank-Wolfe Network: An Interpretable Deep Structure for Non-Sparse CodingCode0
Multi-View Silhouette and Depth Decomposition for High Resolution 3D Object RepresentationCode0
Self Super-Resolution for Magnetic Resonance Images using Deep Networks—0
Residual Dense Network for Image Super-ResolutionCode0
Single Image Super-Resolution via Cascaded Multi-Scale Cross Network—0
MagnifyMe: Aiding Cross Resolution Face Recognition via Identity Aware Synthesis—0
Composite Optimization by Nonconvex Majorization-Minimization—0
Deep Residual Network for Joint Demosaicing and Super-ResolutionCode0
AtlasNet: A Papier-Mâché Approach to Learning 3D Surface GenerationCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified