Phase reconstruction based on deep learning with high pass filtering for holographic data storage

Rongquan Fan; Jianying Hao; Ruixian Chen; Jianan Li; Yongkun Lin; Hongjie Liu; Rupeng Yang; Linlin Fan; Kun Wang; Dakui Lin; Xiao Lin; Xiaodi Tan

doi:10.1117/12.2648345

14 March 2023 Phase reconstruction based on deep learning with high pass filtering for holographic data storage

Rongquan Fan, Jianying Hao, Ruixian Chen, Jianan Li, Yongkun Lin, Hongjie Liu, Rupeng Yang, Linlin Fan, Kun Wang, Dakui Lin, Xiao Lin, Xiaodi Tan

Author Affiliations +

Proceedings Volume 12444, Ultra-High-Definition Imaging Systems VI; 1244404 (2023) https://doi.org/10.1117/12.2648345
Event: SPIE OPTO, 2023, San Francisco, California, United States

Abstract

Compared with traditional iterative methods, deep learning phase reconstruction has lower bit error rate and higher data transfer rate. We found the efficiency of training mainly was from the edges of the phase patterns due to their stronger intensity changes between adjacent phase distribution. According to this characteristic, we proposed a method to only record and use the high frequency component of the phase patterns and to do the deep learning training. This method can improve the storage density due to reducing the material consumption.

Conference Presentation

Citation Download Citation

Rongquan Fan, Jianying Hao, Ruixian Chen, Jianan Li, Yongkun Lin, Hongjie Liu, Rupeng Yang, Linlin Fan, Kun Wang, Dakui Lin, Xiao Lin, and Xiaodi Tan "Phase reconstruction based on deep learning with high pass filtering for holographic data storage", Proc. SPIE 12444, Ultra-High-Definition Imaging Systems VI, 1244404 (14 March 2023); https://doi.org/10.1117/12.2648345

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