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 3161–3170 of 3874 papers

TitleStatusHype
Solving General Noisy Inverse Problem via Posterior Sampling: A Policy Gradient Viewpoint—0
An end-to-end Optical Character Recognition approach for ultra-low-resolution printed text images—0
An End-Cloud Computing Enabled Surveillance Video Transmission System—0
An Empirical Study of Super-resolution on Low-resolution Micro-expression Recognition—0
An Efficient Algorithm for Video Super-Resolution Based On a Sequential Model—0
Video Dynamics Prior: An Internal Learning Approach for Robust Video Enhancements—0
Solving Video Inverse Problems Using Image Diffusion Models—0
Some medical applications of example-based super-resolution—0
A Near-Field Super-Resolution Network for Accelerating Antenna Characterization—0
Sound and Visual Representation Learning with Multiple Pretraining Tasks—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified