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Phase retrieval based on deep learning with bandpass filtering in holographic data storage
Optics Express
|February 1, 2024
Summary
This study introduces a deep learning method for holographic data storage phase retrieval, reducing media consumption by 2.94 times. The technique enhances storage density by filtering specific frequency components.
Area of Science:
- Optics and Photonics
- Data Storage Technologies
- Artificial Intelligence in Engineering
Background:
- Holographic data storage offers high density but faces challenges in phase retrieval accuracy.
- Deep learning approaches show promise for improving phase retrieval efficiency.
Purpose of the Study:
- To develop an efficient phase retrieval method for holographic data storage using deep learning and bandpass filtering.
- To reduce material consumption and enhance storage density by optimizing frequency components.
Main Methods:
- An end-to-end convolutional neural network was trained to establish the relationship between encoded data pages and diffraction intensity patterns.
- Bandpass filtering was applied to attenuate low-frequency components and remove high-order frequencies beyond twice the Nyquist size.
Main Results:
- The training efficiency of deep learning-based phase retrieval is primarily influenced by high-frequency edge details in phase codes.
- Material consumption was reduced by 2.94 times compared to full-frequency recording.
- Storage density was significantly improved due to optimized data processing.
Conclusions:
- Deep learning with bandpass filtering is an effective strategy for phase retrieval in holographic data storage.
- Optimizing frequency components through filtering reduces media usage and enhances storage performance.
- The method demonstrates a practical approach to increasing storage density in holographic systems.
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