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.

To increase the storage capacity in holographic data storage (HDS), the information to be stored is encoded into a complex amplitude. Fast and accurate retrieval of amplitude and phase from the reconstructed beam is necessary during data readout in HDS. In this study, we proposed a complex amplitude demodulation method based on deep learning from a single-shot diffraction intensity image and verified it by a non-interferometric lensless experiment demodulating four-level amplitude and four-level phase. By analyzing the correlation between the diffraction intensity features and the amplitude and phase encoding data pages, the inverse problem was decomposed into two backward operators denoted by two convolutional neural networks (CNNs) to demodulate amplitude and phase respectively. The experimental system is simple, stable, and robust, and it only needs a single diffraction image to realize the direct demodulation of both amplitude and phase. To our investigation, this is the first time in HDS that multilevel complex amplitude demodulation is achieved experimentally from one diffraction intensity image without iterations.

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