Noise reduction reconstruction using deep learning in collinear amplitude holographic data storage

Yongkun Lin; Jianying Hao; Shenghui Ke; Mingyong Chen; Haiyang Song; Hongjie Liu; Xiao Lin; Xiaodi Tan

doi:10.1117/12.2612367

7 March 2022 Noise reduction reconstruction using deep learning in collinear amplitude holographic data storage

Yongkun Lin, Jianying Hao, Shenghui Ke, Mingyong Chen, Haiyang Song, Hongjie Liu, Xiao Lin, Xiaodi Tan

Proceedings Volume 12025, Ultra-High-Definition Imaging Systems V; 120250B (2022) https://doi.org/10.1117/12.2612367
Event: SPIE OPTO, 2022, San Francisco, California, United States

Conference Poster

Abstract

We used the amplitude coding method of 3:16, that is, in a 4 * 4 pixel matrix, only three pixels are in the on state, and the remaining pixels are in the off state. In the collinear amplitude holographic data storage system, U-Net full convolution neural network is used to denoise the amplitude coded image obtained by the detector. The experimental results show that the bit error rate can be reduced to less than 1% from 10% and the image signal-to-noise ratio can be increased by more than 5 times.

Citation Download Citation

Yongkun Lin, Jianying Hao, Shenghui Ke, Mingyong Chen, Haiyang Song, Hongjie Liu, Xiao Lin, and Xiaodi Tan "Noise reduction reconstruction using deep learning in collinear amplitude holographic data storage", Proc. SPIE 12025, Ultra-High-Definition Imaging Systems V, 120250B (7 March 2022); https://doi.org/10.1117/12.2612367

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