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 2271–2280 of 3874 papers

TitleStatusHype
OFDM Reference Signal Pattern Design Criteria for Integrated Communication and Sensing—0
Generating Unobserved Alternatives—0
Generative Adversarial Classifier for Handwriting Characters Super-Resolution—0
Generative Adversarial Models for Extreme Geospatial Downscaling—0
Generative adversarial network for super-resolution imaging through a fiber—0
Generative Adversarial Networks and Perceptual Losses for Video Super-Resolution—0
Generative Adversarial Networks for Image Super-Resolution: A Survey—0
Generative AI in Vision: A Survey on Models, Metrics and Applications—0
Generative Diffusion Prior for Unified Image Restoration and Enhancement—0
A Generative Model for Generic Light Field Reconstruction—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified