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 3301–3310 of 3874 papers

TitleStatusHype
Image Super-Resolution Using Attention Based DenseNet with Residual Deconvolution—0
Hyperspectral Super-Resolution via Global-Local Low-Rank Matrix EstimationCode0
A Single Video Super-Resolution GAN for Multiple Downsampling Operators based on Pseudo-Inverse Image Formation Models—0
CNN-based synthesis of realistic high-resolution LiDAR data—0
Teaching deep neural networks to localize single molecules for super-resolution microscopyCode0
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
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified