Virtual polychromatic DHM: a supervised learning approach for denoising quantitative-phase images and revealing fine subcellular structures (Conference Presentation)

Johan Chaniot; Maxime Moreaud; Céline Larivière-Loiselle; Mohamed Haouat; Marie-Ève Crochetière; Erik Bélanger; Pierre P. Marquet

doi:10.1117/12.2650738

16 March 2023 Virtual polychromatic DHM: a supervised learning approach for denoising quantitative-phase images and revealing fine subcellular structures (Conference Presentation)

Johan Chaniot, Maxime Moreaud, Céline Larivière-Loiselle, Mohamed Haouat, Marie-Ève Crochetière, Erik Bélanger, Pierre P. Marquet

Author Affiliations +

Proceedings Volume PC12389, Quantitative Phase Imaging IX; PC123890E (2023) https://doi.org/10.1117/12.2650738
Event: SPIE BiOS, 2023, San Francisco, California, United States

Abstract

Digital holographic microscopy (DHM), provides an extremely sensitive quantitative-phase signal (QPS), which is nevertheless affected by coherent noise. The recent development of polychromatic DHM (P-DHM) enables us to provide quasi-coherent-noise-free quantitative-phase images. The implementation of P-DHM remains, however, demanding. We propose a convolutional neural network architecture, using for the first time an experimental ground-truth dataset, performing the P-DHM denoising from conventional DHM images. The results highlight, a strong efficiency, fine subcellular structures are made visible without loss of QPS accuracy, an interest in comparison to state-of-the-art learning methods and the possibility of a more widespread use of the P-DHM.

Conference Presentation

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Johan Chaniot, Maxime Moreaud, Céline Larivière-Loiselle, Mohamed Haouat, Marie-Ève Crochetière, Erik Bélanger, and Pierre P. Marquet "Virtual polychromatic DHM: a supervised learning approach for denoising quantitative-phase images and revealing fine subcellular structures (Conference Presentation)", Proc. SPIE PC12389, Quantitative Phase Imaging IX, PC123890E (16 March 2023); https://doi.org/10.1117/12.2650738

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