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 2721–2730 of 3874 papers

TitleStatusHype
Can Super Resolution be used to improve Human Pose Estimation in Low Resolution Scenarios?—0
Super-resolution in Molecular Dynamics Trajectory Reconstruction with Bi-Directional Neural Networks—0
Super-resolution meets machine learning: approximation of measures—0
Super-resolution Method for Coherent DOA Estimation of Multiple Wideband Sources—0
Superresolution method for data deconvolution by superposition of point sources—0
Super-resolution method using sparse regularization for point-spread function recovery—0
Super-resolution MRI Using Finite Rate of Innovation Curves—0
Super-resolution multi-contrast unbiased eye atlases with deep probabilistic refinement—0
Super Resolution of Arterial Spin Labeling MR Imaging Using Unsupervised Multi-Scale Generative Adversarial Network—0
Super-resolution of biomedical volumes with 2D supervision—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified