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<p>Self-referential holography (SRH), a holographic technique that enables the recording, reading, and control of two-dimensional (2D) patterns using a one-beam geometry, can be applied to holographic data storage (HDS) and optoelectronic deep neural network (OE-DNN). Since both applications are implemented using the same optical system, they can be integrated into a single system. We propose a self-referential HDS (SR-HDS) with a built-in denoising function using a self-referential holographic deep neural network (SR-HDNN), where the quality of reconstructed datapages in HDS can be enhanced using deep neural networks (DNNs) without requiring costly electronic computers for implementation. Numerical simulations are performed to demonstrate the feasibility of the proposed method.</p>

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Numerical simulations on self-referential holographic data storage with built-in denoising function by self-referential holographic deep neural network

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  • 出版模式：

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  • 发表日期：

    2025年6月21日

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  • Yuta Eto,
  • Rio Tomioka,
  • Taichi Takatsu,
  • Masanori Takabayashi

摘要

Self-referential holography (SRH), a holographic technique that enables the recording, reading, and control of two-dimensional (2D) patterns using a one-beam geometry, can be applied to holographic data storage (HDS) and optoelectronic deep neural network (OE-DNN). Since both applications are implemented using the same optical system, they can be integrated into a single system. We propose a self-referential HDS (SR-HDS) with a built-in denoising function using a self-referential holographic deep neural network (SR-HDNN), where the quality of reconstructed datapages in HDS can be enhanced using deep neural networks (DNNs) without requiring costly electronic computers for implementation. Numerical simulations are performed to demonstrate the feasibility of the proposed method.