Dense-U-net: Dense encoder–decoder network for holographic imaging of 3D particle fields
Digital holographic imaging is able to reconstruct phase and three-dimensional (3D) information of an object from a one-shot two-dimensional (2D) lensless hologram. A dense encoder–decoder network, called Dense-U-net, is proposed to realize the reconstruction of a 3D particle field. The radii and 3D...
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| Published in: | Optics communications Vol. 493; p. 126970 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier B.V
15.08.2021
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| ISSN: | 0030-4018, 1873-0310 |
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| Abstract | Digital holographic imaging is able to reconstruct phase and three-dimensional (3D) information of an object from a one-shot two-dimensional (2D) lensless hologram. A dense encoder–decoder network, called Dense-U-net, is proposed to realize the reconstruction of a 3D particle field. The radii and 3D coordinates of the particles are encoded into a 2D grayscale image as the ground truth. The 2D hologram was served as the input of the network. A Dense_Block is designed and added to the encoder and decoder of the traditional U-net model. Each layer takes the inputs from all previous layers and passes its own feature map to all subsequent layers. Thus, as the diffraction is distributed throughout the entire hologram, both the features with high signal-to-noise ratio at the center of the particle and the diffraction features with low signal-to-noise ratio around the particle can be learned, thereby facilitating the full characterization of the particles. Using the specially designed dense connection module, network training can be accomplished with fewer parameters and faster speeds.
•A dense encoder–decoder network is proposed to realize reconstruction of 3D particle field.•The Dense-U-net network needs few training parameters and short training time.•A new way is proposed to generate the dataset for digital holographic networks. |
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| AbstractList | Digital holographic imaging is able to reconstruct phase and three-dimensional (3D) information of an object from a one-shot two-dimensional (2D) lensless hologram. A dense encoder–decoder network, called Dense-U-net, is proposed to realize the reconstruction of a 3D particle field. The radii and 3D coordinates of the particles are encoded into a 2D grayscale image as the ground truth. The 2D hologram was served as the input of the network. A Dense_Block is designed and added to the encoder and decoder of the traditional U-net model. Each layer takes the inputs from all previous layers and passes its own feature map to all subsequent layers. Thus, as the diffraction is distributed throughout the entire hologram, both the features with high signal-to-noise ratio at the center of the particle and the diffraction features with low signal-to-noise ratio around the particle can be learned, thereby facilitating the full characterization of the particles. Using the specially designed dense connection module, network training can be accomplished with fewer parameters and faster speeds.
•A dense encoder–decoder network is proposed to realize reconstruction of 3D particle field.•The Dense-U-net network needs few training parameters and short training time.•A new way is proposed to generate the dataset for digital holographic networks. |
| ArticleNumber | 126970 |
| Author | Wu, Yufeng Cao, Liangcai Wu, Jiachen Jin, Shangzhong Jin, Guofan |
| Author_xml | – sequence: 1 givenname: Yufeng surname: Wu fullname: Wu, Yufeng organization: College of Optical and Electronic Technology, China Jiliang University, Hangzhou, Zhejiang 310018, China – sequence: 2 givenname: Jiachen surname: Wu fullname: Wu, Jiachen organization: State Key Laboratory of Precision Measurement Technology and Instruments, Department of Precision Instruments, Tsinghua University, Beijing 100084, China – sequence: 3 givenname: Shangzhong surname: Jin fullname: Jin, Shangzhong email: jinsz@cjlu.edu.cn organization: College of Optical and Electronic Technology, China Jiliang University, Hangzhou, Zhejiang 310018, China – sequence: 4 givenname: Liangcai surname: Cao fullname: Cao, Liangcai email: clc@tsinghua.edu.cn, P1804085245@cjlu.edu.cn organization: State Key Laboratory of Precision Measurement Technology and Instruments, Department of Precision Instruments, Tsinghua University, Beijing 100084, China – sequence: 5 givenname: Guofan surname: Jin fullname: Jin, Guofan organization: State Key Laboratory of Precision Measurement Technology and Instruments, Department of Precision Instruments, Tsinghua University, Beijing 100084, China |
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