Lensless phase retrieval based on deep learning used in holographic data storage

This paper proposes a lensless phase retrieval method based on deep learning (DL) used in holographic data storage. By training an end-to-end convolutional neural network between the phase-encoded data pages and the corresponding near-field diffraction intensity images, the new unknown phase data page can be predicted directly from the intensity image by the network model without any iterations. The DL-based phase retrieval method has a higher storage density, lower bit-error-rate (BER), and higher data transfer rate compared to traditional iterative methods. The retrieval optical system is simple, stable, and robust to environment fluctuations which is suitable for holographic data storage. Besides, we studied and demonstrated that the DL method has a good suppression effect on the dynamic noise of the holographic data storage system. (C) 2021 Optical Society of America

Automated summary

This paper introduces a lensless phase retrieval method based on deep learning for holographic data storage, achieving higher storage density, lower bit-error-rate (BER), and higher data transfer rate. The method features a simple, stable retrieval optical system that is robust to environmental fluctuations and effectively suppresses dynamic noise in the holographic data storage system.