Deep learning method for image segmentation based on encoding features in collinear holographic data storage

Yongkun Lin; Jianying Hao; Shenghui Ke; Haiyang Song; Hongjie Liu; Ruixian Chen; Jing Xu; Xiao Lin; Xiaodi Tan

doi:10.1117/12.3005330

13 March 2024 Deep learning method for image segmentation based on encoding features in collinear holographic data storage

Yongkun Lin, Jianying Hao, Shenghui Ke, Haiyang Song, Hongjie Liu, Ruixian Chen, Jing Xu, Xiao Lin, Xiaodi Tan

Author Affiliations +

Proceedings Volume 12909, Ultra-High-Definition Imaging Systems VII; 1290906 (2024) https://doi.org/10.1117/12.3005330
Event: SPIE OPTO, 2024, San Francisco, California, United States

Abstract

Experiments have shown that deep learning can improve the data reading of holographic data storage. However, it requires a large amount of storage materials and time to obtain data to optimize the network model. In data encoding, each encoded data page consists of 51sub-pages with the same structure. This paper proposes a deep learning method for image segmentation based on encoding features in collinear holographic data storage. Using a deep learning method of image segmentation, the encoded data page is segmented into data sub-pages. It can reduce material loss and data collection time.

Conference Presentation

Citation Download Citation

Yongkun Lin, Jianying Hao, Shenghui Ke, Haiyang Song, Hongjie Liu, Ruixian Chen, Jing Xu, Xiao Lin, and Xiaodi Tan "Deep learning method for image segmentation based on encoding features in collinear holographic data storage", Proc. SPIE 12909, Ultra-High-Definition Imaging Systems VII, 1290906 (13 March 2024); https://doi.org/10.1117/12.3005330

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