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Dual-task convolutional neural network based on the combination of the U-Net and a diffraction propagation model for phase hologram design with suppressed speckle noise

文献类型：期刊论文

作者Sun, Xiuhui2,3; Mu, Xingyu4; Xu, Cheng5,6; Pang, Hui5,6; Deng, Qiling5; Zhang, Ke4; Jiang, Haibo3; Du, Jinglei2; Yin, Shaoyun3; Du, Chunlei1,2
刊名OPTICS EXPRESS
出版日期2022-01-17
卷号30期号:2页码:2646-2658
ISSN号1094-4087
DOI10.1364/OE.440956
通讯作者Yin, Shaoyun(ysy@cigit.ac.cn)
英文摘要In this paper, a dual-task convolutional neural network based on the combination of the U-Net and a diffraction propagation model is proposed for the design of phase holograms to suppress speckle noise of the reconstructed images. By introducing a Fresnel transmission layer, based on angular spectrum diffraction theory, as the diffraction propagation model and incorporating it into U-Net as the output layer, the proposed neural network model can describe the actual physical process of holographic imaging, and the distributions of both the light amplitude and phase can be generated. Afterwards, by respectively using the Pearson correlation coefficient (PCC) as the loss function to modulate the distribution of the amplitude, and a proposed target-weighted standard deviation (TWSD) as the loss function to limit the randomness and arbitrariness of the reconstructed phase distribution, the dual tasks of the amplitude reconstruction and phase smoothing are jointly solved, and thus the phase hologram that can produce high quality image without speckle is obtained. Both simulations and optical experiments are carried out to confirm the feasibility and effectiveness of the proposed method. Furthermore, the depth of field (DOF) of the image using the proposed method is much larger than that of using the traditional Gerchberg-Saxton (GS) algorithm due to the smoothness of the reconstructed phase distribution, which is also verified in the experiments. This study provides a new phase hologram design approach and shows the potential of neural networks in the field of the holographic imaging and more. (C) 2022 Optical Society of America under the terms of the OSA Open Access Publishing Agreement
资助项目Chongqing Science and Technology Commission[cstc2019jscx-mbdxX0019] ; National Natural Science Foundation of China[6217032539] ; National Key Research and Development Program of China[2017YFB1002902]
WOS研究方向Optics
语种英语
WOS记录号WOS:000745037500156
出版者OPTICAL SOC AMER
源URL[http://119.78.100.138/handle/2HOD01W0/15059]  
专题中国科学院重庆绿色智能技术研究院
通讯作者Yin, Shaoyun
作者单位1.Yangtze Normal Univ, Sch Elect Informat Engn, Chongqing 408400, Peoples R China
2.Sichuan Univ, Phys Dept, 29 Wangjiang Rd, Chengdu 610064, Sichuan, Peoples R China
3.Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, 266 Fangzheng Ave, Chongqing 400714, Peoples R China
4.Chongqing Univ Posts & Telecommun, Chongqing 400065, Peoples R China
5.Chinese Acad Sci, Inst Opt & Elect, Chengdu 610209, Peoples R China
6.Univ Elect Sci & Technol China, Sch Phys, Chengdu 610054, Peoples R China
推荐引用方式
GB/T 7714		 Sun, Xiuhui,Mu, Xingyu,Xu, Cheng,et al. Dual-task convolutional neural network based on the combination of the U-Net and a diffraction propagation model for phase hologram design with suppressed speckle noise[J]. OPTICS EXPRESS,2022,30(2):2646-2658.
APA Sun, Xiuhui.,Mu, Xingyu.,Xu, Cheng.,Pang, Hui.,Deng, Qiling.,...&Du, Chunlei.(2022).Dual-task convolutional neural network based on the combination of the U-Net and a diffraction propagation model for phase hologram design with suppressed speckle noise.OPTICS EXPRESS,30(2),2646-2658.
MLA Sun, Xiuhui,et al."Dual-task convolutional neural network based on the combination of the U-Net and a diffraction propagation model for phase hologram design with suppressed speckle noise".OPTICS EXPRESS 30.2(2022):2646-2658.

入库方式： OAI收割

来源：重庆绿色智能技术研究院

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