Dynamic coherent diffractive imaging with a physics-driven untrained learning method | |
Yang, Dongyu1,2; Zhang, Junhao1,2; Tao, Ye1,2; Lv, Wenjin1,2; Lu, Shun3; Chen, Hao2; Xu, Wenhui4,5; Shi, Yishi1,2 | |
刊名 | OPTICS EXPRESS |
2021-09-27 | |
卷号 | 29期号:20页码:31426-31442 |
ISSN号 | 1094-4087 |
DOI | 10.1364/OE.433507 |
英文摘要 | 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 |
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 |
语种 | 英语 |
出版者 | OPTICAL SOC AMER |
WOS记录号 | WOS:000702060000036 |
内容类型 | 期刊论文 |
源URL | [http://119.78.100.204/handle/2XEOYT63/17050] |
专题 | 中国科学院计算技术研究所 |
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. |
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