Displacement derivative analysis using deep learning in digital holographic interferometry

Allaparthi Venkata Satya Vithin; Jagadesh Ramaiah; Dhruvam Pandey; Rajshekhar Gannavarpu; Rajshekhar Gannavarpu

doi:10.1364/DH.2022.W2A.7

Digital Holography and 3-D Imaging 2022
Technical Digest Series (Optica Publishing Group, 2022),
paper W2A.7
•https://doi.org/10.1364/DH.2022.W2A.7

Displacement derivative analysis using deep learning in digital holographic interferometry

Allaparthi Venkata Satya Vithin, Jagadesh Ramaiah, Dhruvam Pandey, and Rajshekhar Gannavarpu

Digital Holography and Three-Dimensional Imaging 2022

Cambridge United Kingdom
1–4 August 2022
ISBN: 978-1-957171-12-8

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Joint Poster Session I (W2A)

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A. V. S. Vithin, J. Ramaiah, D. Pandey, and R. Gannavarpu, "Displacement derivative analysis using deep learning in digital holographic interferometry," in Digital Holography and 3-D Imaging 2022, D. Chu, J. Park, C. Cheng, and P. Ferraro, eds., Technical Digest Series (Optica Publishing Group, 2022), paper W2A.7.

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Abstract

In this article, we present deep learning approach to estimate displacement derivatives in digital holographic interferometry. The results show the capability of the proposed method on noisy experimental fringes.

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