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 1791–1800 of 3874 papers

TitleStatusHype
Single Image Super-Resolution Based on Capsule Neural NetworksCode1
Nanoscopic distribution of VAChT and VGLUT3 in striatal cholinergic varicosities suggests colocalization and segregation of the two transporters in synaptic vesicles—0
MuS2: A Real-World Benchmark for Sentinel-2 Multi-Image Super-ResolutionCode0
Rolling Shutter Inversion: Bring Rolling Shutter Images to High Framerate Global Shutter VideoCode1
Imagen Video: High Definition Video Generation with Diffusion Models—0
Accurate Image Restoration with Attention Retractable TransformerCode1
From Face to Natural Image: Learning Real Degradation for Blind Image Super-ResolutionCode1
Make-A-Video: Text-to-Video Generation without Text-Video DataCode1
Deep Sparse and Low-Rank Prior for Hyperspectral Image DenoisingCode0
Multi-scale Attention Network for Single Image Super-ResolutionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified