Phase retrieval based on deep learning in PQ/PMMA material

Abstract

Holographic data storage (HDS), a three-dimensional volume storage technology, is becoming a strong candidate for huge data storage due to its advantages of higher storage density, faster data transfer rate, and longer life. In this paper, we use phenanthraquinone doped polymethyl methacrylate (PQ/PMMA) photopolymer to record the phase data page, and use deep learning to recover the phase. The experimental setup used 532 nm laser wavelength. The phase data page uploaded on the SLM are made up of 4-gray level random phase coded (π/6,2π/3, π,3π/2). Every pixel pitch of SLM is 20 μm. Every phase data is described by some oversampling display such as 8*8 pixels on the SLM. And, the CMOS captured phase data pages using PQ/PMMA photopolymers. We input 4439 randomly generated phase data pages into the experimental system. 4439 phase data pages after rotating 90°, rotating 180°, rotating 270°, upside down, flipped left to right, expanded to 6 times, a total of 26634 phase data pages. Among them, 90 % is used as the training set to optimize the network, 10 % is used as the testing set to verify the generalization ability of the trained neural network. We trained the U-net 50 epoch, and when we got the predicted phase data. And the bit error rate (BER) of the reconstructed image using PQ/PMMA photopolymers were measured, and we found that, different numbers experimental images has different BER. So, deep learning can effectively reduce BER of phase data page by using PQ/PMMA photopolymers.