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