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How holographic imaging can improve machine learning
Author(s): Pasquale Memmolo; Vittorio Bianco; Pierluigi Carcagnì; Francesco Merola; Melania Paturzo; Cosimo Distante; Pietro Ferraro
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Paper Abstract

Nowadays, digital holography can be considered as one of the most powerful imaging modality in several research fields, from the 3D imaging for display purposes to quantitative phase image in microscopy and microfluidics. At the same time, machine learning in imaging applications has been literally reborn to the point of being considered the most exploited field by optical imaging researchers. In fact, the use of deep convolutional neural networks has permitted to achieve impressive results in the classification of biological samples obtained by holographic imaging, as well as for solving inverse problems in holographic microscopy. Definitely, machine learning approaches in digital holography has been used mainly to improve the performance of the imaging tool. Here we show a reverse modality in which holographic imaging boosts the performance of machine leaning algorithms. In particular, we identify several descriptors solely related to the type of data to be classified, i.e. the holographic image. We provide some case studies which demonstrate how the holographic imaging can improve the performance of a plain classifier.

Paper Details

Date Published: 21 June 2019
PDF: 6 pages
Proc. SPIE 11059, Multimodal Sensing: Technologies and Applications, 1105908 (21 June 2019); doi: 10.1117/12.2527480
Show Author Affiliations
Pasquale Memmolo, Istituto di Scienze Applicate e Sistemi Intelligenti "Eduardo Caianiello" (Italy)
Vittorio Bianco, Istituto di Scienze Applicate e Sistemi Intelligenti "Eduardo Caianiello" (Italy)
Pierluigi Carcagnì, Istituto di Scienze Applicate e Sistemi Intelligenti "Eduardo Caianiello" (Italy)
Francesco Merola, Istituto di Scienze Applicate e Sistemi Intelligenti "Eduardo Caianiello" (Italy)
Melania Paturzo, Istituto di Scienze Applicate e Sistemi Intelligenti "Eduardo Caianiello" (Italy)
Cosimo Distante, Istituto di Scienze Applicate e Sistemi Intelligenti "Eduardo Caianiello" (Italy)
Pietro Ferraro, Istituto di Scienze Applicate e Sistemi Intelligenti "Eduardo Caianiello" (Italy)


Published in SPIE Proceedings Vol. 11059:
Multimodal Sensing: Technologies and Applications
Ettore Stella, Editor(s)

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