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 2031–2040 of 3874 papers

TitleStatusHype
Deep Learning for Low-Field to High-Field MR: Image Quality Transfer with Probabilistic Decimation Simulator—0
Deep Learning for Isotropic Super-Resolution from Non-Isotropic 3D Electron Microscopy—0
Adaptive Blind Super-Resolution Network for Spatial-Specific and Spatial-Agnostic Degradations—0
Deep Learning for Inverse Problems: Bounds and Regularizers—0
Learning Discriminative Multilevel Structured Dictionaries for Supervised Image Classification—0
Learning Dual Convolutional Neural Networks for Low-Level Vision—0
Deep Learning for Automatic Strain Quantification in Arrhythmogenic Right Ventricular Cardiomyopathy—0
Learning Efficient Image Super-Resolution Networks via Structure-Regularized Pruning—0
Learning Enriched Features for Fast Image Restoration and Enhancement—0
Deep Learning Enables Large Depth-of-Field Images for Sub-Diffraction-Limit Scanning Superlens Microscopy—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified