利用自指全息深度神经网络对内置去噪功能的自指全息数据存储进行数值模拟

IF 0.9 4区 物理与天体物理 Q4 OPTICS
Yuta Eto, Rio Tomioka, Taichi Takatsu, Masanori Takabayashi
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引用次数: 0

摘要

自指全息(SRH)是一种全息技术,可以使用单光束几何形状记录、读取和控制二维(2D)图案,可应用于全息数据存储(HDS)和光电子深度神经网络(e- dnn)。由于这两个应用程序都是使用相同的光学系统实现的,因此它们可以集成到一个系统中。我们提出了一种使用自指全息深度神经网络(sr - hdn)的内置去噪功能的自指全息数据处理系统(SR-HDS),其中可以使用深度神经网络(dnn)增强HDS中重构数据的质量,而不需要昂贵的电子计算机来实现。通过数值仿真验证了该方法的可行性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Numerical simulations on self-referential holographic data storage with built-in denoising function by self-referential holographic deep neural network

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.

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来源期刊
Optical Review
Optical Review 物理-光学
CiteScore
2.30
自引率
0.00%
发文量
62
审稿时长
2 months
期刊介绍: Optical Review is an international journal published by the Optical Society of Japan. The scope of the journal is: General and physical optics; Quantum optics and spectroscopy; Information optics; Photonics and optoelectronics; Biomedical photonics and biological optics; Lasers; Nonlinear optics; Optical systems and technologies; Optical materials and manufacturing technologies; Vision; Infrared and short wavelength optics; Cross-disciplinary areas such as environmental, energy, food, agriculture and space technologies; Other optical methods and applications.
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