Speed Up of Volumetric Non-Local Transform-Domain Filter Utilising HPC Architecture

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27740%2F23%3A10253391" target="_blank" >RIV/61989100:27740/23:10253391 - isvavai.cz</a>

Result on the web

<a href="https://www.mdpi.com/2313-433X/9/11/254" target="_blank" >https://www.mdpi.com/2313-433X/9/11/254</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/jimaging9110254" target="_blank" >10.3390/jimaging9110254</a>

Result language

angličtina

Original language name

Speed Up of Volumetric Non-Local Transform-Domain Filter Utilising HPC Architecture

Original language description

This paper presents a parallel implementation of a non-local transform-domain filter (BM4D). The effectiveness of the parallel implementation is demonstrated by denoising image series from computed tomography (CT) and magnetic resonance imaging (MRI). The basic idea of the filter is based on grouping and filtering similar data within the image. Due to the high level of similarity and data redundancy, the filter can provide even better denoising quality than current extensively used approaches based on deep learning (DL). In BM4D, cubes of voxels named patches are the essential image elements for filtering. Using voxels instead of pixels means that the area for searching similar patches is large. Because of this and the application of multi-dimensional transformations, the computation time of the filter is exceptionally long. The original implementation of BM4D is only single-threaded. We provide a parallel version of the filter that supports multi-core and many-core processors and scales on such versatile hardware resources, typical for high-performance computing clusters, even if they are concurrently used for the task. Our algorithm uses hybrid parallelisation that combines open multi-processing (OpenMP) and message passing interface (MPI) technologies and provides up to 283x speedup, which is a 99.65% reduction in processing time compared to the sequential version of the algorithm. In denoising quality, the method performs considerably better than recent DL methods on the data type that these methods have yet to be trained on.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

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Continuities

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Publication year

2023

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Journal of Imaging

ISSN

2313-433X

e-ISSN

2313-433X

Volume of the periodical

9

Issue of the periodical within the volume

11

Country of publishing house

CH - SWITZERLAND

Number of pages

15

Pages from-to

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UT code for WoS article

001113330800001

EID of the result in the Scopus database

2-s2.0-85178318511