Deep learning-based design of additional patterns in self-referential holographic data storage

Abstract

Self-referential holographic data storage (SR-HDS), which has been proposed as a novel implementation method for holographic data storage (HDS), enables holographic recording without a reference beam. In addition to the signal pattern (SP) to be recorded, an additional pattern (AP) that affects the reconstruction quality is used in SR-HDS. One of the methods for obtaining a designed AP that contributes to high-quality reconstruction involves utilizing local search algorithms, such as the hill climbing (HC) method. However, designing an AP using this method typically requires a significant amount of time. In this study, we proposed a new AP-designing method that uses a deep neural network. By training a network with pairs of SP and designed AP based on a local search algorithm, a designed AP that improves the reconstruction quality of an arbitrary SP can be instantly obtained. APs designed using the deep learning-based method improved the reconstruction quality of SPs to the same level as those designed using the method based on local search algorithm, whereas the time required to obtain one designed AP was reduced by three or four orders of magnitude.