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$μ$GUIDE a framework for microstructure imaging via generalized uncertainty-driven inference using deep learning

M Jallais, M Palombo - arXiv preprint arXiv:2312.17293, 2023 - arxiv.org
Electrical Engineering and Systems Science paper eess.IV Suggest

… Harnessing a new deep learning architecture for automatic signal feature selection combined with simulationbased inference and efficient sampling of the posterior …

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BibTeX

@misc{jallais2024muguide,
title={$mu$GUIDE a framework for microstructure imaging via generalized uncertainty-driven inference using deep learning},
author={Maëliss Jallais and Marco Palombo},
year={2024},
eprint={2312.17293},
archivePrefix={arXiv},
primaryClass={id='eess.IV' full_name='Image and Video Processing' is_active=True alt_name=None in_archive='eess' is_general=False description='Theory, algorithms, and architectures for the formation, capture, processing, communication, analysis, and display of images, video, and multidimensional signals in a wide variety of applications. Topics of interest include mathematical, statistical, and perceptual image and video modeling and representation; linear and nonlinear filtering, de-blurring, enhancement, restoration, and reconstruction from degraded, low-resolution or tomographic data; lossless and lossy compression and coding; segmentation, alignment, and recognition; image rendering, visualization, and printing; computational imaging, including ultrasound, tomographic and magnetic resonance imaging; and image and video analysis, synthesis, storage, search and retrieval.'}
}

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