Compared with traditional iterative methods, deep learning phase reconstruction has lower bit error rate and higher data transfer rate. We found the efficiency of training mainly was from the edges of the phase patterns due to their stronger intensity changes between adjacent phase distribution. According to this characteristic, we proposed a method to only record and use the high frequency component of the phase patterns and to do the deep learning training. This method can improve the storage density due to reducing the material consumption.
The phase retrieval method based on deep learning can be used to solve the iterative problem in holographic data storage. The key of the deep learning method is to build the relationship between the phase data pages and the corresponding near-field diffraction intensity patterns. However, to build the correct relationship, thousands of samples of the training dataset are usually required. In this paper, according to the coding characteristics of phase data pages, we proposed an image segmentation method to greatly reduce the number of original training dataset. The innovation proposed by this new method lies in the special segmentation of the original samples to expand the number of samples.
This paper analyzed the security of random phase encryption holographic storage technology. Taking binary random phase as an example, the recorded hologram is continually readout by series guessing reference. The experiment showed that the correlation coefficient between readout information and the recorded information was firstly decreased and then increased when the phase correct ratio of guessing reference is increased from 0% to 100%. The recorded information can’t be readout at all when the phase correct ratio of guessing reference range from 40% to 60%. Since the guessing reference with phase correct ratio between 40% and 60% has occupied majority guessing cases, the recorded information can’t be cracked in most cases. This indicates the high security of the random phase encryption storage technique.
Research of holographic storage security is of great significance to the development of holographic storage technology. To ensure the difficulty of cracking, the data reconstructed by the wrong key should present a statistically independent random noise distribution as far as possible. This paper studies collinear holographic encryption storage based on the orthogonal Hadamard matrix and random phase. After storing data with a particular key A in a regular ring shape, the secret key A can reconstruct the data. However, some other keys can also reconstruct partial data (crosstalk noise), and this crosstalk greatly reduced the security of the data storage system. Here, random orthogonal phase coding is proposed to solve the crosstalk problem, and the reference light was equally divided into 64 pieces. Each one consists of the same number of pixels at random positions in the circular reference light. The randomness of each reference pixel ensures the consistency of the reconstructed data light intensity, and the data can be completely eliminated due to the orthogonality of the reference light. The orthogonal reconstructed data presents a nearly statistical independent noise distribution, which has effectively reduced the similarity between the original data and the reconstructed data by a wrong key, avoided data leakage, and improved the security of holographic encryption storage.
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