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 3271–3280 of 3874 papers

TitleStatusHype
A CNN-Based Super-Resolution Technique for Active Fire Detection on Sentinel-2 Data—0
Resolution-invariant Person Re-IdentificationCode0
DeepSTORM3D: dense three dimensional localization microscopy and point spread function design by deep learningCode0
Algorithmic Guarantees for Inverse Imaging with Untrained Network PriorsCode0
Image Super Resolution via Bilinear Pooling: Application to Confocal Endomicroscopy—0
Exemplar Guided Face Image Super-Resolution without Facial LandmarksCode0
Hierarchical Back Projection Network for Image Super-ResolutionCode0
On training deep networks for satellite image super-resolution—0
Back-Projection based Fidelity Term for Ill-Posed Linear Inverse ProblemsCode0
Single Image Super-resolution via Dense Blended Attention Generative Adversarial Network for Clinical Diagnosis—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified