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 2021–2030 of 3874 papers

TitleStatusHype
Deep Learning Techniques for Super-Resolution in Video Games—0
Deep Learning Super-Resolution Enables Rapid Simultaneous Morphological and Quantitative Magnetic Resonance Imaging—0
Super-resolution of Ray-tracing Channel Simulation via Attention Mechanism based Deep Learning Model—0
Deep learning in ultrasound imaging—0
Learning Correction Errors via Frequency-Self Attention for Blind Image Super-Resolution—0
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
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified