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Holographic Information Processing
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Holographic Information Processing

A special issue of Photonics (ISSN 2304-6732).

Deadline for manuscript submissions: 31 October 2024 | Viewed by 4499

Special Issue Editors

Dr. Xianfeng Xu

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Guest Editor
College of Science, China University of Petroleum (East China), Qingdao, China
Interests: digital holography and deep learning
Dr. Hongbo Zhang

E-Mail Website
Guest Editor
Engineering Technology, Middle Tennessee State University, Murfreesboro, TN, USA
Interests: digital holography
Dr. Wen-Jing Zhou

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Guest Editor
Department of Precision Mechanical Engineering, Shanghai University, Shanghai 200444, China
Interests: digital holography
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Ting-Chung Poon

E-Mail Website
Guest Editor
Bradley Department of Electrical and Computer Engineering, Virginia Tech, Blacksburg, VA 24061, USA
Interests: digital holography specializing on optical scanning holography; 3-D optical image processing and holographic display; computer-generated holography; holographic remote sensing; holographic microscopy
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleague,

This is a Special Issue concerned with digital holography. With this Special Issue, we call for research papers in the areas of coherent and incoherent digital holography, optical scanning holography, holographic display, holographic cryptography, and holographic information processing in general.

Research using machine learning, deep learning, and other artificial intelligence technology for holographic information processing is especially welcomed. Both fundamental and applied research studies are suitable for this Special Issue.

We are looking forward to receiving your manuscripts.

Dr. Xianfeng Xu
Dr. Hongbo Zhang
Dr. Wen-Jing Zhou
Prof. Dr. Ting-Chung Poon
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Photonics is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (5 papers)

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Research

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9 pages, 4388 KiB  
Article
Diffractive Deep-Neural-Network-Based Classifier for Holographic Memory
by Toshihiro Sakurai, Tomoyoshi Ito and Tomoyoshi Shimobaba
Photonics 2024, 11(2), 145; https://0-doi-org.brum.beds.ac.uk/10.3390/photonics11020145 - 04 Feb 2024
Viewed by 798
Abstract
Holographic memory offers high-capacity optical storage with rapid data readout and long-term durability. Recently, read data pages have been classified using digital deep neural networks (DNNs). This approach is highly accurate, but the prediction time hinders the data readout throughput. This study presents [...] Read more.
Holographic memory offers high-capacity optical storage with rapid data readout and long-term durability. Recently, read data pages have been classified using digital deep neural networks (DNNs). This approach is highly accurate, but the prediction time hinders the data readout throughput. This study presents a diffractive DNN (D2NN)-based classifier for holographic memory. D2NNs have so far attracted a great deal of attention for object identification and image transformation at the speed of light. A D2NN, consisting of trainable diffractive layers and devoid of electronic devices, facilitates high-speed data readout. Furthermore, we numerically investigated the classification performance of a D2NN-based classifier. The classification accuracy of the D2NN was 99.7% on 4-bit symbols, exceeding that of the hard decision method. Full article
(This article belongs to the Special Issue Holographic Information Processing)
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12 pages, 3776 KiB  
Article
Reducing the Crosstalk in Collinear Holographic Data Storage Systems Based on Random Position Orthogonal Phase-Coding Reference
by Haiyang Song, Jianan Li, Dakui Lin, Hongjie Liu, Yongkun Lin, Jianying Hao, Kun Wang, Xiao Lin and Xiaodi Tan
Photonics 2023, 10(10), 1160; https://0-doi-org.brum.beds.ac.uk/10.3390/photonics10101160 - 16 Oct 2023
Viewed by 832
Abstract
Previous studies have shown that orthogonal phase-coding multiplexing performs well with low crosstalk in conventional off-axis systems. However, noticeable crosstalk occurs when applying the orthogonal phase-coding multiplexing to collinear holographic data storage systems. This paper demonstrates the crosstalk generation mechanism, features, and elimination [...] Read more.
Previous studies have shown that orthogonal phase-coding multiplexing performs well with low crosstalk in conventional off-axis systems. However, noticeable crosstalk occurs when applying the orthogonal phase-coding multiplexing to collinear holographic data storage systems. This paper demonstrates the crosstalk generation mechanism, features, and elimination methods. The crosstalk is caused by an inconsistency in the intensity reconstruction from the orthogonal phase-coded reference wave. The intensity fluctuation range was approximately 40%. Moreover, the more concentrated the distribution of pixels with the same phase key, the more pronounced the crosstalk. We propose an effective random orthogonal phase-coding reference wave method to reduce the crosstalk. The orthogonal phase-coded reference wave is randomly distributed over the entire reference wave. These disordered orthogonal phase-coded reference waves achieve consistent reconstruction intensities exhibiting the desired low-crosstalk storage effect. The average correlation coefficient between pages decreased by 73%, and the similarity decreased by 85%. This orthogonal phase-coding multiplexing method can be applied to encrypted holographic data storage. The low-crosstalk nature of this technique will make the encryption system more secure. Full article
(This article belongs to the Special Issue Holographic Information Processing)
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13 pages, 15322 KiB  
Article
Robust Holographic Reconstruction by Deep Learning with One Frame
by Xianfeng Xu, Weilong Luo, Hao Wang and Xinwei Wang
Photonics 2023, 10(10), 1155; https://0-doi-org.brum.beds.ac.uk/10.3390/photonics10101155 - 15 Oct 2023
Viewed by 919
Abstract
A robust method is proposed to reconstruct images with only one hologram in digital holography by introducing a deep learning (DL) network. The U-net neural network is designed according to DL principles and trained by the image data set collected using phase-shifting digital [...] Read more.
A robust method is proposed to reconstruct images with only one hologram in digital holography by introducing a deep learning (DL) network. The U-net neural network is designed according to DL principles and trained by the image data set collected using phase-shifting digital holography (PSDH). The training data set was established by collecting thousands of reconstructed images using PSDH. The proposed method can complete the holography reconstruction with only a single hologram and then benefits the space bandwidth product and relaxes the storage loads of PSDH. Compared with the results of PSDH, the results of deep learning are immune to most disturbances, including reference tilt, phase-shift errors, and speckle noise. Assisted by a GPU processor, the proposed reconstruction method can reduce the consumption time to almost one percent of the time needed by two-step PSDH. This method is expected to be capable of holography imaging with a single hologram, with high capacity, efficiently in the digital holography applications. Full article
(This article belongs to the Special Issue Holographic Information Processing)
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9 pages, 3074 KiB  
Communication
Isotropic Two-Dimensional Differentiation Based on Dual Dynamic Volume Holograms
by Pin Wang, Houxin Fan, Yaping Zhang, Yongwei Yao, Bing Zhang, Wenlong Qin and Ting-Chung Poon
Photonics 2023, 10(7), 828; https://0-doi-org.brum.beds.ac.uk/10.3390/photonics10070828 - 17 Jul 2023
Cited by 1 | Viewed by 641
Abstract
We study the use of two dynamic thick holograms to realize isotropic two-dimensional (2D) differentiation under Bragg diffraction. Acousto-optic modulators (AOMs) are used as dynamic volume holograms. Using a single volume hologram, we can accomplish a first-order derivative operation, corresponding to selective edge [...] Read more.
We study the use of two dynamic thick holograms to realize isotropic two-dimensional (2D) differentiation under Bragg diffraction. Acousto-optic modulators (AOMs) are used as dynamic volume holograms. Using a single volume hologram, we can accomplish a first-order derivative operation, corresponding to selective edge extraction of an image. Since the AOM is a 1D spatial light modulator, filtering of the image only occurs along the direction of the sound propagation. To achieve 2D image processing, two AOMs are used within a Mach–Zehnder interferometer (MZI). By aligning one AOM along the x-direction on the upper arm of the interferometer and another AOM along the y-direction on the lower arm, we accomplish the sum of two first-derivative operations, leading to isotropic edge extraction. We have performed both computer simulations and optical experiments to verify the proposed idea. The system provides additional operations in optical computing using AOMs as dynamic holograms. Full article
(This article belongs to the Special Issue Holographic Information Processing)
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Review

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19 pages, 12947 KiB  
Review
Computational Optical Scanning Holography
by Naru Yoneda, Jung-Ping Liu, Osamu Matoba, Yusuke Saita and Takanori Nomura
Photonics 2024, 11(4), 347; https://0-doi-org.brum.beds.ac.uk/10.3390/photonics11040347 - 10 Apr 2024
Viewed by 502
Abstract
Holographic techniques are indispensable tools for modern optical engineering. Over the past two decades, research about incoherent digital holography has continued to attract attention. Optical scanning holography (OSH) can obtain incoherent holograms using single-pixel detection and structured illumination with Fresnel zone patterns (FZPs). [...] Read more.
Holographic techniques are indispensable tools for modern optical engineering. Over the past two decades, research about incoherent digital holography has continued to attract attention. Optical scanning holography (OSH) can obtain incoherent holograms using single-pixel detection and structured illumination with Fresnel zone patterns (FZPs). Particularly by changing the size of a detector, OSH can also obtain holograms under coherently illuminated conditions. Since 1979, OSH has continuously evolved. According to the evolution of semiconductor technology, spatial light modulators (SLMs) come to be useful for various imaging fields. By using SLM techniques for OSH, the practicality of OSH is improved. These SLM-based OSH methods are termed computational OSH (COSH). In this review, the configurations, recording and reconstruction methods, and proposed applications of COSH are reviewed. Full article
(This article belongs to the Special Issue Holographic Information Processing)
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