Dynamic coherent diffractive imaging with a physics-driven untrained learning method

doi:10.1364/OE.433507

	Dynamic coherent diffractive imaging with a physics-driven untrained learning method
	Yang, Dongyu 1,2; Zhang, Junhao 1,2; Tao, Ye 1,2; Lv, Wenjin 1,2; Lu, Shun 3; Chen, Hao 2; Xu, Wenhui 4,5; Shi, Yishi 1,2
	2021-09-27
发表期刊	OPTICS EXPRESS
ISSN	1094-4087
卷号	29 期号:20 页码:31426-31442
摘要	Reconstruction of a complex field from one single diffraction measurement remains a challenging task among the community of coherent diffraction imaging (CDI). Conventional iterative algorithms are time-consuming and struggle to converge to a feasible solution because of the inherent ambiguities. Recently, deep-learning-based methods have shown considerable success in computational imaging, but they require large amounts of training data that in many cases are difficult to obtain. Here, we introduce a physics-driven untrained learning method, termed Deep CDI, which addresses the above problem and can image a dynamic process with high confidence and fast reconstruction. Without any labeled data for pretraining, the Deep CDI can reconstruct a complex-valued object from a single diffraction pattern by combining a conventional artificial neural network with a real-world physical imaging model. To our knowledge, we are the first to demonstrate that the support region constraint, which is widely used in the iteration-algorithm-based method, can be utilized for loss calculation. The loss calculated from support constraint and free propagation constraint are summed up to optimize the network's weights. As a proof of principle, numerical simulations and optical experiments on a static sample are carried out to demonstrate the feasibility of our method. We then continuously collect 3600 diffraction patterns and demonstrate that our method can predict the dynamic process with an average reconstruction speed of 228 frames per second (FPS) using only a fraction of the diffraction data to train the weights. (C) 2021 Optical Society of America under the terms of the OSA Open Access Publishing Agreement
DOI	10.1364/OE.433507
收录类别	SCI
语种	英语
资助项目	Youth Innovation Promotion Association of the Chinese Academy of Sciences[2017489] ; University of Chinese Academy of Sciences ; Fusion Foundation of Research and Education of CAS ; Natural Science Foundation of Hebei Province[F2018402285] ; National Natural Science Foundation of China[61575197]
WOS研究方向	Optics
WOS类目	Optics
WOS记录号	WOS:000702060000036
出版者	OPTICAL SOC AMER
引用统计	被引频次：17[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/17051
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Shi, Yishi
作者单位	1.Univ Chinese Acad Sci, Sch Optoelect, Beijing 100049, Peoples R China 2.Univ Chinese Acad Sci, Ctr Mat Sci & Optoelect Engn, Beijing 100049, Peoples R China 3.Chinese Acad Sci, Inst Comp Technol, Beijing 100049, Peoples R China 4.Southern Univ Sci & Technol, Dept Elect & Elect Engn, Shenzhen 518055, Peoples R China 5.Harbin Inst Technol, Harbin 150001, Peoples R China
推荐引用方式 GB/T 7714	Yang, Dongyu,Zhang, Junhao,Tao, Ye,et al. Dynamic coherent diffractive imaging with a physics-driven untrained learning method[J]. OPTICS EXPRESS,2021,29(20):31426-31442.
APA	Yang, Dongyu.,Zhang, Junhao.,Tao, Ye.,Lv, Wenjin.,Lu, Shun.,...&Shi, Yishi.(2021).Dynamic coherent diffractive imaging with a physics-driven untrained learning method.OPTICS EXPRESS,29(20),31426-31442.
MLA	Yang, Dongyu,et al."Dynamic coherent diffractive imaging with a physics-driven untrained learning method".OPTICS EXPRESS 29.20(2021):31426-31442.