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 3831–3840 of 3874 papers

TitleStatusHype
Bayesian Sparse Representation for Hyperspectral Image Super Resolution—0
An Efficient Algorithm for Video Super-Resolution Based On a Sequential Model—0
Geometry-Aware Neighborhood Search for Learning Local Models for Image Reconstruction—0
Self-Tuned Deep Super Resolution—0
Novel Super-Resolution Method Based on High Order Nonlocal-Means—0
Single image super-resolution by approximated Heaviside functions—0
Designing A Composite Dictionary Adaptively From Joint Examples—0
Simple, Accurate, and Robust Nonparametric Blind Super-Resolution—0
Parallel Statistical Multi-resolution Estimation—0
Learning Super-Resolution Jointly from External and Internal Examples—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified