Lensless coaxial digital holographic imaging based on open-source hardware and deep learning

Junxue Wang; Lichen Lin; Shiqi Liu; Suodong Ma; Xianmeng Shen

doi:10.1117/12.2666519

27 January 2023 Lensless coaxial digital holographic imaging based on open-source hardware and deep learning

Junxue Wang, Lichen Lin, Shiqi Liu, Suodong Ma, Xianmeng Shen

Proceedings Volume 12550, International Conference on Optical and Photonic Engineering (icOPEN 2022); 125501M (2023) https://doi.org/10.1117/12.2666519
Event: International Conference on Optical and Photonic Engineering (icOPEN 2022), 2022, ONLINE, China

Abstract

Lensless Coaxial Digital Holographic Imaging (LCDHI) has great advantages of wide-field, high resolution and non-destructive measurement. By removing traditional lenses and utilizing a coaxial optical path, the lens-aberration can be avoided, and the imaging process is greatly simplified, which is one of the powerful tools for observing of micro components and biological cells. With the help of an open-source hardware and deep learning technology, a simple and portable experimental device based on the principle of lensless coaxial digital holography is designed and set up in this study. In order to avoid the problems of difficult data-acquisition and time-consuming training caused by supervised learning, a deep convolutional neural network (CNN) based on an auto-encoder is embedded into the Gerchberg-Saxton (GS) iterative process of LCDHI to accomplish phase retrieval. Compared with the traditional GS algorithm, more accurate amplitude and phase results can be reconstructed by the proposed low-cost device and the designed CNN through several experiments.

Citation Download Citation

Junxue Wang, Lichen Lin, Shiqi Liu, Suodong Ma, and Xianmeng Shen "Lensless coaxial digital holographic imaging based on open-source hardware and deep learning", Proc. SPIE 12550, International Conference on Optical and Photonic Engineering (icOPEN 2022), 125501M (27 January 2023); https://doi.org/10.1117/12.2666519

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