Phase retrieval network for multi-functional holography based on self-supervised learning

Jialei Xie; Lei Jin

doi:10.1117/12.3023658

18 March 2024 Phase retrieval network for multi-functional holography based on self-supervised learning

Jialei Xie, Lei Jin

Proceedings Volume 13104, Advanced Fiber Laser Conference (AFL2023); 1310456 (2024) https://doi.org/10.1117/12.3023658
Event: Advanced Fiber Laser Conference (AFL2023), 2023, Shenzhen, China

Abstract

Phase-only hologram reconstructs desired field distribution by modulating the phase of incident light, which is an important method for realizing full-color or 3-dimensional (3D) display. Under a given condition, traditional iterative algorithms or data-driven neural networks are employed to retrieve the phase profiles of full-color or 3D holograms. However, traditional iterative algorithms are required to repeat the optimization process for different target images, while the performance of data-driven neural network is limited by the training dataset. In this work, we propose a phase retrieval network for multi-functional holography based on self-supervised learning. The proposed method based on physics inspired network is formed by image preprocessing, deep neural network (DNN), and diffraction propagation. After self-supervised training, the proposed network retrieves the phase profile by inputting the target images, working wavelengths, and image distances. Compared with traditional iterative algorithms, the proposed network accelerates the phase retrieval process, suppresses the sparkle noise and improves the quality of reconstructed images. This proposed method has a great potential to realize real time 3D full-color display.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jialei Xie and Lei Jin "Phase retrieval network for multi-functional holography based on self-supervised learning", Proc. SPIE 13104, Advanced Fiber Laser Conference (AFL2023), 1310456 (18 March 2024); https://doi.org/10.1117/12.3023658

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available