Laser & Optoelectronics Progress, Volume. 60, Issue 18, 1811002(2023)
Continuous-Wave Terahertz In-Line Digital Holography Based on Physics-Enhanced Deep Neural Network
Terahertz (THz) in-line digital holography is a promising full-field, lens-free, and quantitative phase-contrast imaging method with an extremely compact and stable optical configuration. Hence, it is suitable for the application of THz waves. However, the inherent twin-image problem can impair the quality of its reconstructions. In this study, a novel learning-based iterative phase retrieval algorithm, termed as physics-enhanced deep neural network (PhysenNet), is introduced. This method combines a physical model with a convolutional neural network to mitigate the twin-image issue in THz waves. Notably, PhysenNet can reconstruct the complex fields of a sample with high fidelity from just a single in-line digital hologram, without the need for constraints or a pre-training labeled dataset. Based on simulations and experimental results, it is evident that PhysenNet surpasses existing phase retrieval algorithms in imaging quality, further enhancing the application range of THz in-line digital holography.
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Jie Zhao, Xiaoyu Jin, Dayong Wang, Lu Rong, Yunxin Wang, Shufeng Lin. Continuous-Wave Terahertz In-Line Digital Holography Based on Physics-Enhanced Deep Neural Network[J]. Laser & Optoelectronics Progress, 2023, 60(18): 1811002
Category: Imaging Systems
Received: May. 30, 2023
Accepted: Aug. 8, 2023
Published Online: Sep. 6, 2023
The Author Email: Zhao Jie (zhaojie@bjut.edu.cn), Wang Dayong (wdyong@bjut.edu.cn)