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 2241–2250 of 3874 papers

TitleStatusHype
A Scale-Arbitrary Image Super-Resolution Network Using Frequency-domain Information—0
ADIR: Adaptive Diffusion for Image Reconstruction—0
Double U-Net for Super-Resolution and Segmentation of Live Cell Images—0
Region-Conditioned Orthogonal 3D U-Net for Weather4Cast CompetitionCode0
Downscaling Extreme Rainfall Using Physical-Statistical Generative Adversarial Learning—0
DiTBN: Detail Injection-Based Two-Branch Network for Pansharpening of Remote Sensing ImagesCode0
Super-resolution of positive near-colliding point sources—0
ATASI-Net: An Efficient Sparse Reconstruction Network for Tomographic SAR Imaging with Adaptive Threshold—0
Adaptive adversarial training method for improving multi-scale GAN based on generalization bound theory—0
MrSARP: A Hierarchical Deep Generative Prior for SAR Image Super-resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified