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 3861–3870 of 3874 papers

TitleStatusHype
Simultaneous Super-Resolution of Depth and Images Using a Single Camera—0
On a Link Between Kernel Mean Maps and Fraunhofer Diffraction, with an Application to Super-Resolution Beyond the Diffraction Limit—0
Fast Image Super-Resolution Based on In-Place Example Regression—0
Beta Process Joint Dictionary Learning for Coupled Feature Spaces with Application to Single Image Super-Resolution—0
Depth Super Resolution by Rigid Body Self-Similarity in 3D—0
Robust Spectral Compressed Sensing via Structured Matrix Completion—0
A Convex Approach for Image Hallucination—0
A Joint Intensity and Depth Co-Sparse Analysis Model for Depth Map Super-Resolution—0
Spectral Compressed Sensing via Structured Matrix Completion—0
Robust image reconstruction from multi-view measurements—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified