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 1981–1990 of 3874 papers

TitleStatusHype
Residual Local Feature Network for Efficient Super-ResolutionCode1
Combating COVID-19 using Generative Adversarial Networks and Artificial Intelligence for Medical Images: A Scoping Review—0
Nearly optimal resolution estimate for the two-dimensional super-resolution and a new algorithm for direction of arrival estimation with uniform rectangular array—0
Evaluating the Generalization Ability of Super-Resolution Networks—0
Unsupervised Representation Learning for 3D MRI Super Resolution with Degradation Adaptation—0
Tensor Decompositions for Hyperspectral Data Processing in Remote Sensing: A Comprehensive Review—0
Blueprint Separable Residual Network for Efficient Image Super-ResolutionCode1
AFFIRM: Affinity Fusion-based Framework for Iteratively Random Motion correction of multi-slice fetal brain MRICode0
NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and ResultsCode1
Spatial-Temporal Space Hand-in-Hand: Spatial-Temporal Video Super-Resolution via Cycle-Projected Mutual Learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified