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 2591–2600 of 3874 papers

TitleStatusHype
Sound and Visual Representation Learning with Multiple Pretraining Tasks—0
SOUP-GAN: Super-Resolution MRI Using Generative Adversarial Networks—0
Source Identification: A Self-Supervision Task for Dense Prediction—0
Space-Time Distillation for Video Super-Resolution—0
Space-Time Video Super-resolution with Neural Operator—0
SparseAlign: A Super-Resolution Algorithm for Automatic Marker Localization and Deformation Estimation in Cryo-Electron Tomography—0
Sparse-based Domain Adaptation Network for OCTA Image Super-Resolution Reconstruction—0
Sparse Based Super Resolution Multilayer Ultrasonic Array Imaging—0
Sparse Coding Approach for Multi-Frame Image Super Resolution—0
Sparse Depth Super Resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified