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 1981–1990 of 3874 papers

TitleStatusHype
On Versatile Video Coding at UHD with Machine-Learning-Based Super-Resolution—0
Revolutionizing Space Health (Swin-FSR): Advancing Super-Resolution of Fundus Images for SANS Visual Assessment TechnologyCode0
A Self-Supervised Deep Denoiser for Hyperspectral and Multispectral Image FusionCode0
Iterative Reweighted Least Squares Networks With Convergence Guarantees for Solving Inverse Imaging Problems—0
HSD-PAM: High Speed Super Resolution Deep Penetration Photoacoustic Microscopy Imaging Boosted by Dual Branch Fusion Network—0
Harnessing Artificial Intelligence To Reduce Phototoxicity in Live Imaging—0
PSRFlow: Probabilistic Super Resolution with Flow-Based Models for Scientific Data—0
When Super-Resolution Meets Camouflaged Object Detection: A Comparison Study—0
High-Resolution Vision Transformers for Pixel-Level Identification of Structural Components and Damage—0
All-in-one Multi-degradation Image Restoration Network via Hierarchical Degradation Representation—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified