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 631–640 of 3874 papers

TitleStatusHype
Stop-and-go wave super-resolution reconstruction via iterative refinement—0
Image Super-Resolution with Taylor Expansion Approximation and Large Field Reception—0
Efficient Channel Estimation for Millimeter Wave and Terahertz Systems Enabled by Integrated Super-resolution Sensing and Communication—0
What makes for good morphology representations for spatial omics?—0
Deep Learning for Super-resolution Ultrasound Imaging with Spatiotemporal Data—0
Efficient Face Super-Resolution via Wavelet-based Feature Enhancement NetworkCode2
Competition-based Adaptive ReLU for Deep Neural Networks—0
Inverse Problems with Diffusion Models: A MAP Estimation PerspectiveCode0
Sewer Image Super-Resolution with Depth Priors and Its Lightweight Network—0
Super Resolution for Renewable Energy Resource Data With Wind From Reanalysis Data (Sup3rWind) and Application to Ukraine—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified