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 1261–1270 of 3874 papers

TitleStatusHype
CurvPnP: Plug-and-play Blind Image Restoration with Deep Curvature DenoiserCode0
Enhancing wind field resolution in complex terrain through a knowledge-driven machine learning approachCode0
A Systematic Investigation on Deep Learning-Based Omnidirectional Image and Video Super-ResolutionCode0
Machine learning for reconstruction of polarity inversion lines from solar filamentsCode0
MAANet: Multi-view Aware Attention Networks for Image Super-ResolutionCode0
Maintaining Natural Image Statistics with the Contextual LossCode0
Frank-Wolfe Network: An Interpretable Deep Structure for Non-Sparse CodingCode0
Localized Super Resolution for Foreground Images using U-Net and MR-CNNCode0
L-MAGIC: Language Model Assisted Generation of Images with CoherenceCode0
Cross-Resolution Learning for Face RecognitionCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified