Single-shot complex amplitude demodulation based on deep learning

Jianying Hao; Xiao Lin; Yongkun Lin; Ruixian Chen; Xiaodi Tan; Yuhong Ren

doi:10.1117/12.2659021

8 December 2022 Single-shot complex amplitude demodulation based on deep learning

Jianying Hao, Xiao Lin, Yongkun Lin, Ruixian Chen, Xiaodi Tan, Yuhong Ren

Proceedings Volume 12479, Optical Manipulation and Structured Materials Conference (OMC 2022); 124790K (2022) https://doi.org/10.1117/12.2659021
Event: Optics and Photonics International Congress, 2022, Yokohama, Japan

Abstract

This paper proposes a complex amplitude demodulation method based on deep learning used in holographic data storage (HDS). To increase the storage capacity of a single data page in HDS, the complex amplitude of the object light can be used to encode the information data. However, the phase information of the complex amplitude cannot be detected directly. In this paper, we propose a non-interferometric complex amplitude retrieval method based on deep learning that can demodulate amplitude and phase simultaneously. A one-to-two convolutional neural network (CNN) is designed to establish the relationship between the intensity images captured by the detector and complex amplitude data pages. A simulation experiment is established to verify the feasibility of the proposed method.

Citation Download Citation

Jianying Hao, Xiao Lin, Yongkun Lin, Ruixian Chen, Xiaodi Tan, and Yuhong Ren "Single-shot complex amplitude demodulation based on deep learning", Proc. SPIE 12479, Optical Manipulation and Structured Materials Conference (OMC 2022), 124790K (8 December 2022); https://doi.org/10.1117/12.2659021

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