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 2971–2980 of 3874 papers

TitleStatusHype
MXR-U-Nets for Real Time Hyperspectral ReconstructionCode1
4DFlowNet: Super-Resolution 4D Flow MRI using Deep Learning and Computational Fluid DynamicsCode1
Mosaic Super-resolution via Sequential Feature Pyramid Networks—0
Multi-modal Datasets for Super-resolution—0
KD-MRI: A knowledge distillation framework for image reconstruction and image restoration in MRI workflowCode1
D-SRGAN: DEM Super-Resolution with Generative Adversarial Networks—0
DeepSEE: Deep Disentangled Semantic Explorative Extreme Super-ResolutionCode1
Time accelerated image super-resolution using shallow residual feature representative network—0
Monte-Carlo Siamese Policy on Actor for Satellite Image Super Resolution—0
Image super-resolution reconstruction based on attention mechanism and feature fusion—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified