https://doi.org/10.1140/epjp/s13360-024-05791-y
Regular Article
GPU-based data processing for speeding-up correlation plenoptic imaging
1
Planetek Italia s.r.l., 70132, Bari, Italy
2
Dipartimento Interateneo di Fisica, Università degli Studi di Bari, 70125, Bari, Italy
3
INFN - Sezione di Bari, 70125, Bari, Italy
4
Planetek Hellas E.P.E., 15125, Athens, Marousi, Greece
5
École polytechnique fédérale de Lausanne (EPFL), 2002, Neuchâtel, Switzerland
a
gianlorenzo.massaro@uniba.It
Received:
30
July
2024
Accepted:
30
October
2024
Published online:
5
December
2024
Correlation plenoptic imaging (CPI) is a novel technological imaging modality enabling to overcome drawbacks of standard plenoptic devices, while preserving their advantages. However, a major challenge in view of real-time application of CPI is related to the relevant amount of required frames and the consequent computational-intensive processing algorithm. In this work, we describe the design and implementation of an optimized processing algorithm that is portable to an efficient computational environment and exploits the highly parallel algorithm offered by GPUs. Improvements by a factor ranging from 20X, for correlation measurement, to 500X, for refocusing, are demonstrated. Exploration of the relation between the improvement in performance achieved and actual GPU capabilities also indicates the feasibility of near-real-time processing capability, opening up to the potential use of CPI for practical real-time application.
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© The Author(s), under exclusive licence to Società Italiana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature 2024
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
