How holographic imaging can improve machine learning

Pasquale Memmolo; Vittorio Bianco; Pierluigi Carcagnì; Francesco Merola; Melania Paturzo; Cosimo Distante; Pietro Ferraro

doi:10.1117/12.2527480

21 June 2019 How holographic imaging can improve machine learning

Pasquale Memmolo, Vittorio Bianco, Pierluigi Carcagnì, Francesco Merola, Melania Paturzo, Cosimo Distante, Pietro Ferraro

Author Affiliations +

Proceedings Volume 11059, Multimodal Sensing: Technologies and Applications; 1105908 (2019) https://doi.org/10.1117/12.2527480
Event: SPIE Optical Metrology, 2019, Munich, Germany

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.

Conference Presentation

Citation Download Citation

Pasquale Memmolo, Vittorio Bianco, Pierluigi Carcagnì, Francesco Merola, Melania Paturzo, Cosimo Distante, and Pietro Ferraro "How holographic imaging can improve machine learning", Proc. SPIE 11059, Multimodal Sensing: Technologies and Applications, 1105908 (21 June 2019); https://doi.org/10.1117/12.2527480

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