ABSTRACT
Based on the tensor polarization holography theory, we propose a simple and convenient method in the recording material, phenanthrenequinone-doped polymethylmethacrylate, to generate beams on higher and hybrid-order Poincaré spheres, and realize their polarization evolution on the spheres by combining the recorded phase with the Pancharatnam-Berry phase. By simultaneously adjusting the polarization azimuth angle and relative phase of the recorded waves, independent phase-shifts can be imparted onto two orthogonal circular polarization states in reconstruction process of polarization holography. The beams on basic Poincaré sphere are transformed into that on arbitrary higher or hybrid-order Poincaré spheres. We get the Poincaré spheres' type and polarization distribution of the reconstructed wave by interferometry and polarizer, and the results match well with the theoretical predictions.
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.
