Abstract
In an amplitude-modulated collinear holographic data storage system, optical system aberration and experimental noise due to the recording medium often result in a high bit error rate (BER) and low signal-to-noise ratio (SNR) in directly read detector data. This study proposes an anti-noise performance analysis using deep learning. End-to-end convolutional neural networks were employed to analyze noise resistance in encoded data pages captured by the detector. Experimental results demonstrate that these networks effectively correct system imaging aberrations, detector light intensity response, holographic storage medium response non-uniformity, and defocusing noise from the recording objective lens. Consequently, the BER of reconstructed encoded data pages can be reduced to 1/10 of that from direct detection, while the SNR can be increased more than fivefold, enhancing the accuracy and reliability of data reading in amplitude holographic data storage systems.
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