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 2026–2050 of 3874 papers

TitleStatusHype
Deep Learning Framework for Infrastructure Maintenance: Crack Detection and High-Resolution Imaging of Infrastructure Surfaces—0
Learning Coupled Dictionaries from Unpaired Data for Image Super-Resolution—0
Deep Learning for Super-resolution Ultrasound Imaging with Spatiotemporal Data—0
Learning Deep Analysis Dictionaries for Image Super-Resolution—0
Learning Deep Analysis Dictionaries -- Part II: Convolutional Dictionaries—0
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
Learning Exhaustive Correlation for Spectral Super-Resolution: Where Spatial-Spectral Attention Meets Linear Dependence—0
Deep Learning-based Synthetic High-Resolution In-Depth Imaging Using an Attachable Dual-element Endoscopic Ultrasound Probe—0
Deep learning-based super-resolution in coherent imaging systems—0
Learning for Unconstrained Space-Time Video Super-Resolution—0
Deep Learning based Super-Resolution for Medical Volume Visualization with Direct Volume Rendering—0
Learning Frequency-aware Dynamic Network for Efficient Super-Resolution—0
GRNN:Recurrent Neural Network based on Ghost Features for Video Super-Resolution—0
Deep Learning based Optical Image Super-Resolution via Generative Diffusion Models for Layerwise in-situ LPBF Monitoring—0
Learning from Irregularly Sampled Data for Endomicroscopy Super-resolution: A Comparative Study of Sparse and Dense Approaches—0
Learning from Multi-Perception Features for Real-Word Image Super-resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified