Deep learning-based super-resolution holographic data storage

Jianying Hao; Xiao Lin; Ryushi Fujimura; Soki Hirayama; Yoshito Tanaka; Xiaodi Tan; Tsutomu Shimura

doi:10.1117/12.3008355

20 September 2023 Deep learning-based super-resolution holographic data storage

Jianying Hao, Xiao Lin, Ryushi Fujimura, Soki Hirayama, Yoshito Tanaka, Xiaodi Tan, Tsutomu Shimura

Proceedings Volume 12606, Optical Manipulation and Structured Materials Conference; 126060X (2023) https://doi.org/10.1117/12.3008355
Event: Optics and Photonics International Congress, 2023, Yokohama, Japan

Abstract

In this paper, we propose a super-resolution holographic data storage system based on deep learning. A low-pass filter was introduced into the Fourier plane to remove the high frequency. This produces a blurred intensity image of the reconstructed beam. A convolutional neural network is used to establish the relationship between the blurred intensity image and the data page. The encoded phase data page can be directly demodulated from a captured intensity image, which is a non-interferometric method without iterations. The function of the filter is to generate the blurred intensity image and to reduce the recording area to improve the recording intensity. Usually, the limit of the aperture is the Nyquist size. Here, by introducing embedded data on the phase data page, the aperture size of the recording can be reduced to smaller than the Nyquist size. A simulation experiment was established to verify the effectiveness of the proposed method.

Citation Download Citation

Jianying Hao, Xiao Lin, Ryushi Fujimura, Soki Hirayama, Yoshito Tanaka, Xiaodi Tan, and Tsutomu Shimura "Deep learning-based super-resolution holographic data storage ", Proc. SPIE 12606, Optical Manipulation and Structured Materials Conference, 126060X (20 September 2023); https://doi.org/10.1117/12.3008355

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