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

The result's identifiers

  • 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>

Alternative languages

  • 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

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • OECD FORD branch

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

Result continuities

  • Project

  • Continuities

Others

  • Publication year

    2023

  • Confidentiality

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

Data specific for result type

  • 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

  • UT code for WoS article

    001113330800001

  • EID of the result in the Scopus database

    2-s2.0-85178318511