ABSTRACT
A phase retrieval method based on deep learning with bandpass filtering in holographic data storage is proposed. The relationship between the known encoded data pages and their near-field diffraction intensity patterns is established by an end-to-end convolutional neural network, which is used to predict the unknown phase data page. We found the training efficiency of phase retrieval by deep learning is mainly determined by the edge details of the adjacent phase codes, which are the high-frequency components of the phase code. Therefore, we can attenuate the low-frequency components to reduce material consumption. Besides, we also filter out the high-order frequency over twice Nyquist size, which is redundant information with poor anti-noise performance. Compared with full-frequency recording, the consumption of storage media is reduced by 2.94 times, thus improving the storage density.
ABSTRACT
We propose a simple and inexpensive method for the fabrication of polarization splitters with designable separation angles and a controllable active area, based on polarization holography of tensor theory. First, we design two polarization holograms that reconstruct waves with only p- or s-polarization components, respectively. Then, after we recorded these two holograms on the same position of the recording material using the interference approach, as a result, a polarization splitter could readily be prepared. The separation angles of fabricated polarization splitters can be easily adjusted by changing the interference angle, and the active area can also be easily modified by changing the sizes of the interference beams and recording material during the recording process. The experimental results verify the reliability and accuracy of this method. We believe that this work may broaden the application field of polarization holography.
