Deep learning image segmentation method based on coding characteristics

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

doi:10.1117/12.2649851

14 March 2023 Deep learning image segmentation method based on coding characteristics

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

Author Affiliations +

Proceedings Volume 12444, Ultra-High-Definition Imaging Systems VI; 1244403 (2023) https://doi.org/10.1117/12.2649851
Event: SPIE OPTO, 2023, San Francisco, California, United States

Abstract

The phase retrieval method based on deep learning can be used to solve the iterative problem in holographic data storage. The key of the deep learning method is to build the relationship between the phase data pages and the corresponding near-field diffraction intensity patterns. However, to build the correct relationship, thousands of samples of the training dataset are usually required. In this paper, according to the coding characteristics of phase data pages, we proposed an image segmentation method to greatly reduce the number of original training dataset. The innovation proposed by this new method lies in the special segmentation of the original samples to expand the number of samples.

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Citation Download Citation

Ruixian Chen, Jianying Hao, Jinyu Wang, Jianan Li, Yongkun Lin, Hongjie Liu, Rupeng Yang, Rongquan Fan, Kun Wang, Dakui Lin, Xiaodi Tan, and Xiao Lin "Deep learning image segmentation method based on coding characteristics", Proc. SPIE 12444, Ultra-High-Definition Imaging Systems VI, 1244403 (14 March 2023); https://doi.org/10.1117/12.2649851

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