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 2951–2960 of 3874 papers

TitleStatusHype
Unsupervised Video Understanding by Reconciliation of Posture Similarities—0
Untrained, physics-informed neural networks for structured illumination microscopy—0
Unveiling Hidden Details: A RAW Data-Enhanced Paradigm for Real-World Super-Resolution—0
Urban precipitation downscaling using deep learning: a smart city application over Austin, Texas, USA—0
Use of triplet loss for facial restoration in low-resolution images—0
USF Spectral Estimation: Prevalence of Gaussian Cramér-Rao Bounds Despite Modulo Folding—0
USIM-DAL: Uncertainty-aware Statistical Image Modeling-based Dense Active Learning for Super-resolution—0
Using Physics-Informed Super-Resolution Generative Adversarial Networks for Subgrid Modeling in Turbulent Reactive Flows—0
Using super-resolution for enhancing visual perception and segmentation performance in veterinary cytology—0
Using Super-Resolution Imaging for Recognition of Low-Resolution Blurred License Plates: A Comparative Study of Real-ESRGAN, A-ESRGAN, and StarSRGAN—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified