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 2091–2100 of 3874 papers

TitleStatusHype
DeepDPM: Dynamic Population Mapping via Deep Neural Network—0
Learning Super-resolution 3D Segmentation of Plant Root MRI Images from Few Examples—0
Learning Super-Resolution Jointly from External and Internal Examples—0
Learning Super-Resolution Ultrasound Localization Microscopy from Radio-Frequency Data—0
Learning Texture Transformer Network for Light Field Super-Resolution—0
Deep Depth Super-Resolution : Learning Depth Super-Resolution using Deep Convolutional Neural Network—0
Deep Convolutional Neural Network for Multi-modal Image Restoration and Fusion—0
Deep Blind Hyperspectral Image Fusion—0
Learning the Non-Differentiable Optimization for Blind Super-Resolution—0
Learning to Become an Expert: Deep Networks Applied To Super-Resolution Microscopy—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified