Real-time hologram generation using a non-iterative modified Gerchberg-Saxton algorithm
Computer-generated holography (CGH) is a technique that aims to produce specific illumination patterns using coherent light. However, traditional CGH algorithms often struggle to balance computational speed with the accuracy of the generated hologram. To address this issue, a non-iterative algorithm...
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| Published in: | Optics communications Vol. 550; p. 130024 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier B.V
01.01.2024
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| ISSN: | 0030-4018, 1873-0310 |
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| Abstract | Computer-generated holography (CGH) is a technique that aims to produce specific illumination patterns using coherent light. However, traditional CGH algorithms often struggle to balance computational speed with the accuracy of the generated hologram. To address this issue, a non-iterative algorithm named "DL-GSA" is proposed in this paper. DL-GSA combines unsupervised learning in machine learning with convolutional neural networks (CNN) to generate holograms with high accuracy and fixed computational complexity. Simulation experiments reveal that DL-GSA generates hologram patterns faster than the Modified Gerchberg-Saxton algorithm (MGSA) and double-phase retrieval algorithm (DPRA). Furthermore, the average accuracy of the generated holograms is higher than 95 %. These findings suggest that DL-GSA has the potential to significantly enhance the real-time hologram generation capabilities, making it a promising technique for future applications. |
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| AbstractList | Computer-generated holography (CGH) is a technique that aims to produce specific illumination patterns using coherent light. However, traditional CGH algorithms often struggle to balance computational speed with the accuracy of the generated hologram. To address this issue, a non-iterative algorithm named "DL-GSA" is proposed in this paper. DL-GSA combines unsupervised learning in machine learning with convolutional neural networks (CNN) to generate holograms with high accuracy and fixed computational complexity. Simulation experiments reveal that DL-GSA generates hologram patterns faster than the Modified Gerchberg-Saxton algorithm (MGSA) and double-phase retrieval algorithm (DPRA). Furthermore, the average accuracy of the generated holograms is higher than 95 %. These findings suggest that DL-GSA has the potential to significantly enhance the real-time hologram generation capabilities, making it a promising technique for future applications. |
| ArticleNumber | 130024 |
| Author | Chuang, Chih-Hao Cheng, Ching-Wen Chen, Chien-Yu Chou, Tzu-An |
| Author_xml | – sequence: 1 givenname: Chien-Yu surname: Chen fullname: Chen, Chien-Yu organization: Graduate Institute of Color and Illumination Technology, National Taiwan University of Science and Technology, Taipei, 10607, Taiwan – sequence: 2 givenname: Ching-Wen surname: Cheng fullname: Cheng, Ching-Wen organization: Graduate Institute of Color and Illumination Technology, National Taiwan University of Science and Technology, Taipei, 10607, Taiwan – sequence: 3 givenname: Tzu-An surname: Chou fullname: Chou, Tzu-An organization: Graduate Institute of Electro-Optical Engineering, National Taiwan University of Science and Technology, Taipei City, 10607, Taiwan – sequence: 4 givenname: Chih-Hao orcidid: 0000-0002-3291-5913 surname: Chuang fullname: Chuang, Chih-Hao email: zx610438@gmail.com organization: Department of Photonics, Feng Chia University, 100 Wenhwa Rd., Seatwen, Taichung, 40724, Taiwan |
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| Keywords | Deep learning Non-iterative algorithm Computer-generated hologram Real-time hologram generation |
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