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 2701–2710 of 3874 papers

TitleStatusHype
Privacy-Preserving Human Activity Recognition from Extreme Low Resolution—0
Private Eye: On the Limits of Textual Screen Peeking via Eyeglass Reflections in Video Conferencing—0
Probabilistic Motion Modeling from Medical Image Sequences: Application to Cardiac Cine-MRI—0
Precipitation Downscaling with Spatiotemporal Video Diffusion—0
Probabilistic Super-Resolution of Solar Magnetograms: Generating Many Explanations and Measuring Uncertainties—0
An Optimal Transport Perspective on Unpaired Image Super-Resolution—0
Process of image super-resolution—0
ProfileSR-GAN: A GAN based Super-Resolution Method for Generating High-Resolution Load Profiles—0
Unpaired MRI Super Resolution with Contrastive Learning—0
Blind Hyperspectral-Multispectral Image Fusion via Graph Laplacian Regularization—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified