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 2981–2990 of 3874 papers

TitleStatusHype
Artefact removal in ground truth deficient fluctuations-based nanoscopy images using deep learning—0
Scale-Agnostic Super-Resolution in MRI using Feature-Based Coordinate Networks—0
Scale-arbitrary Invertible Image Downscaling—0
A Robust Super-resolution Gridless Imaging Framework for UAV-borne SAR Tomography—0
Scale-aware Super-resolution Network with Dual Affinity Learning for Lesion Segmentation from Medical Images—0
Use of triplet loss for facial restoration in low-resolution images—0
Scale-Invariant Adversarial Attack against Arbitrary-scale Super-resolution—0
Scale-Transferrable Object Detection—0
A robust single-pixel particle image velocimetry based on fully convolutional networks with cross-correlation embedded—0
USF Spectral Estimation: Prevalence of Gaussian Cramér-Rao Bounds Despite Modulo Folding—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified