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MRI denoising by nonlocal means on multi-GPU

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Abstract

A critical issue in image restoration is noise removal, whose state-of-art algorithm, NonLocal Means, is highly demanding in terms of computational time. Aim of the present paper is to boost its performance by an efficient algorithm tailored to GPU hardware architectures. This algorithm adapts itself to several variants of the methodologies in terms of different strategies for estimating the involved filtering parameter, type of noise affecting data, multicomponent signals, spatial dimension of the images. Numerical experiments on brain Magnetic Resonance images are provided.

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Notes

  1. Experiments on the spatially adaptive noise variance are run for the only purpose of evaluating the computational time, since the considered data have spatially uniform noise variance.

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Authors and Affiliations

  1. Istituto per le Applicazioni del Calcolo ‘Mauro Picone’ CNR, Naples, Italy

    Donatella Granata & Umberto Amato

  2. Istituto di Biostrutture e Bioimmagini CNR, Naples, Italy

    Bruno Alfano

Authors
  1. Donatella Granata
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  2. Umberto Amato
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  3. Bruno Alfano
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Corresponding author

Correspondence to Donatella Granata.

Additional information

Work done under the project MEdical Research in ITaly RBNE08E8CZ and POR Campania FESR 2007/2013 project Bersagli funded by the Italian Ministry of University and Research.

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Granata, D., Amato, U. & Alfano, B. MRI denoising by nonlocal means on multi-GPU. J Real-Time Image Proc 16, 523–533 (2019). https://doi.org/10.1007/s11554-016-0566-2

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  • DOI: https://doi.org/10.1007/s11554-016-0566-2

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