Benchmark of deep learning approaches for phase denoising in digital holography

Silvio Montresor; Ketao Yan; Marie Tahon; Kemao Qian; Yingjie Yu; Pascal Picart

doi:10.1364/DH.2023.HW3C.4

Optica Imaging Congress (3D, COSI, DH, FLatOptics, IS, pcAOP)
Technical Digest Series (Optica Publishing Group, 2023),
paper HW3C.4
•https://doi.org/10.1364/DH.2023.HW3C.4

Benchmark of deep learning approaches for phase denoising in digital holography

Silvio Montresor, Ketao Yan, Marie Tahon, Kemao Qian, Yingjie Yu, and Pascal Picart

Digital Holography and Three-Dimensional Imaging 2023

Boston, Massachusetts United States
14–17 August 2023
ISBN: 978-1-957171-28-9

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Phase Processing, Retrieval Techniques, and Applications III (HW3C)

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S. Montresor, K. Yan, M. Tahon, K. Qian, Y. Yu, and P. Picart, "Benchmark of deep learning approaches for phase denoising in digital holography," in Optica Imaging Congress (3D, COSI, DH, FLatOptics, IS, pcAOP), Technical Digest Series (Optica Publishing Group, 2023), paper HW3C.4.

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Abstract

This paper presents a comparative study of deep learning based algorithms to de-noise wrapped phase maps in digital holography interferometry. In order to compare two deep neural networks trained on two different databases, we propose to train both networks on both databases. The four resulting networks are then benchmarked with one unique database. We present the assessment between two models developed in Python. A third model developed in matlab is iadded in evaluation presented in this paper but will be not subject to retraining in the second step of the benchmark.

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