Deep learning method for image segmentation based on encoding features in collinear holographic data storage

Experiments have shown that deep learning can improve the data reading of holographic data storage. However, it requires a large amount of storage materials and time to obtain data to optimize the network model. In data encoding, each encoded data page consists of 51sub-pages with the same structure. This paper proposes a deep learning method for image segmentation based on encoding features in collinear holographic data storage. Using a deep learning method of image segmentation, the encoded data page is segmented into data sub-pages. It can reduce material loss and data collection time.