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Convolution-error-free model-driven neural network for phase-only computer-generated hologram
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Zehao HE1, Kexuan LIU2, Liangcai CAO2, *, Yan ZHANG1, *
Science & Technology Review | 2025, 43(5) : 107 - 116
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Science & Technology Review | 2025, 43(5): 107-116
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Convolution-error-free model-driven neural network for phase-only computer-generated hologram
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Zehao HE1, Kexuan LIU2, Liangcai CAO2, *, Yan ZHANG1, *
Affiliations
  • 1. Department of Physics, Capital Normal University, Beijing 100048, China
  • 2. Department of Precision Instrument, Tsinghua University, Beijing 100084, China
Published: 2025-03-13 doi: 10.3981/j.issn.1000-7857.2024.12.01771
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Computer-generated holography is a distinguished and promising choice for dynamic three-dimensional display. Currently, the greatest challenge in computer-generated holography lies in the inability of holographic algorithms to simultaneously achieve both high precision and speed. To address this challenge, a convolution-error-free model-driven neural network is proposed in this work, enabling the fast generation of high-fidelity phase-only computer-generated holograms. Firstly, the mechanism of convolution errors in the angular spectrum method is analyzed. A modified angular spectrum method that eliminates convolution errors is proposed, along with an iterative framework based on this modified method. The effectiveness of the error-free angular spectrum method in enhancing the calculation precision of phase-only holograms is successfully demonstrated. Secondly, a model-driven neural network is developed by incorporating the convolution-error-free angular spectrum method as the decoder. This network achieves a reduction of calculation time for generating phase-only holograms by 3 orders of magnitude. The phase-only holograms generated by this network effectively suppress speckle noise and enhance detail quality in optical reconstructions, achieving an average peak signal-to-noise ratio (PSNR) of 20.38 dB. With ongoing advancements in depth gradient saliency and channel efficiency consistency, the proposed network holds significant potential for widespread application in areas including virtual reality, metaverse, and three-dimensional video communication.

computer-generated holography  /  three-dimensional display  /  deep learning  /  neural network  /  convolution error
Zehao HE, Kexuan LIU, Liangcai CAO, Yan ZHANG. Convolution-error-free model-driven neural network for phase-only computer-generated hologram[J]. Science & Technology Review, 2025 , 43 (5) : 107 -116 . DOI: 10.3981/j.issn.1000-7857.2024.12.01771
Year 2025 volume 43 Issue 5
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doi: 10.3981/j.issn.1000-7857.2024.12.01771
  • Receive Date:2024-12-22
  • Online Date:2025-06-29
  • Published:2025-03-13
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  • Received:2024-12-22
  • Revised:2025-01-09
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    1. Department of Physics, Capital Normal University, Beijing 100048, China
    2. Department of Precision Instrument, Tsinghua University, Beijing 100084, China
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小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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