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 2041–2050 of 3874 papers

TitleStatusHype
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