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Image segmentation of phase-modulated holographic data storage based on deep learning.

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    This study introduces an image segmentation technique to significantly reduce the data needed for training deep learning (DL) models in holographic data storage (HDS). This method makes DL-based phase retrieval more practical for HDS applications.

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    Area of Science:

    • Optics
    • Data Storage
    • Artificial Intelligence

    Background:

    • Deep learning (DL) offers efficient phase retrieval for phase-modulated holographic data storage (HDS).
    • Traditional DL training for HDS requires extensive sample data, hindering practical application.
    • Current methods for DL-based phase retrieval in HDS are data-intensive.

    Purpose of the Study:

    • To develop a more efficient training method for DL-based phase retrieval in HDS.
    • To reduce the number of required training samples for DL networks in HDS.
    • To enhance the practicality of DL for holographic data storage.

    Main Methods:

    • An image segmentation method utilizing image features was proposed.
    • The method significantly reduces the number of original sample pairs (OSP) needed for DL network training.
    • The approach is designed for easy implementation in HDS systems.

    Main Results:

    • The proposed image segmentation method achieved approximately a 54-fold reduction in training sample pairs.
    • This substantial reduction makes DL-based phase retrieval more feasible for HDS.
    • The method maintains the efficiency of DL for retrieving phase information from diffraction intensity images.

    Conclusions:

    • The developed image segmentation technique effectively minimizes training data requirements for DL in HDS.
    • This advancement simplifies and accelerates the adoption of DL for phase retrieval in holographic data storage.
    • The method offers a practical solution for overcoming data limitations in DL-based HDS.