Related Experiment Video
Updated: Jun 6, 2025
10:16
Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects
Published on: February 8, 2014
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Anti-noise performance analysis in amplitude-modulated collinear holographic data storage using deep learning
Optics Express
|November 22, 2024
Summary
Deep learning effectively reduces noise in holographic data storage. Convolutional neural networks improve signal-to-noise ratio and lower bit error rates for reliable data retrieval.
Area of Science:
- Optics and Photonics
- Data Storage Technologies
- Artificial Intelligence in Engineering
Background:
- Holographic data storage systems face challenges with high bit error rates (BER) and low signal-to-noise ratios (SNR) due to optical aberrations and experimental noise.
- Direct detection methods in amplitude-modulated collinear holographic storage are susceptible to various noise sources, impacting data integrity.
Purpose of the Study:
- To propose and analyze the anti-noise performance of deep learning methods for holographic data storage.
- To investigate the effectiveness of end-to-end convolutional neural networks in mitigating noise and improving data reconstruction accuracy.
Main Methods:
- Utilized end-to-end convolutional neural networks (CNNs) for analyzing noise resistance in encoded data pages captured by detectors.
- Applied deep learning models to correct for system imaging aberrations, detector non-uniformity, and optical defocusing noise.
Main Results:
- Deep learning networks demonstrated effective correction of various noise sources, including optical aberrations and detector response non-uniformity.
- Reconstructed data pages showed a significant reduction in bit error rate (BER), decreased to 1/10th of direct detection levels.
- Signal-to-noise ratio (SNR) was enhanced more than fivefold, indicating improved data readability.
Conclusions:
- Deep learning, specifically CNNs, offers a robust solution for enhancing data reliability in amplitude holographic data storage systems.
- The proposed anti-noise analysis using deep learning significantly improves the accuracy and trustworthiness of data retrieval from holographic storage media.
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