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 3141–3150 of 3874 papers

TitleStatusHype
Harnessing Sparsity over the Continuum: Atomic Norm Minimization for Super Resolution—0
Rapid Whole-Heart CMR with Single Volume Super-resolution—0
Joint Face Super-Resolution and Deblurring Using a Generative Adversarial Network—0
Exploiting Style and Attention in Real-World Super-Resolution—0
Analyzing an Imitation Learning Network for Fundus Image Registration Using a Divide-and-Conquer Approach—0
Anisotropic Super Resolution in Prostate MRI using Super Resolution Generative Adversarial Networks—0
An Application of Generative Adversarial Networks for Super Resolution Medical Imaging—0
Lightweight and Robust Representation of Economic Scales from Satellite ImageryCode0
Adaptive Densely Connected Super-Resolution ReconstructionCode0
FISR: Deep Joint Frame Interpolation and Super-Resolution with a Multi-scale Temporal LossCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified