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 3821–3830 of 3874 papers

TitleStatusHype
Boosting Optical Character Recognition: A Super-Resolution Approach—0
Handling Motion Blur in Multi-Frame Super-Resolution—0
An Efficient Algorithm for Video Super-Resolution Based On a Sequential Model—0
Robust Image Filtering Using Joint Static and Dynamic Guidance—0
Bayesian Sparse Representation for Hyperspectral Image Super Resolution—0
Modeling Deformable Gradient Compositions for Single-Image Super-Resolution—0
Metric Imitation by Manifold Transfer for Efficient Vision Applications—0
Single Image Super-Resolution From Transformed Self-Exemplars—0
Robust Multi-Image Based Blind Face Hallucination—0
Transport-Based Single Frame Super Resolution of Very Low Resolution Face Images—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified