Deep learning assisted variational Hilbert quantitative phase imaging
深度学习辅助变换希尔伯特定量相位成像
深さ学習支援変分ヒルベルト定量位相イメージング
딥러닝 보조 변분 힐버트 정량 위상 영상
Imagen de fase cuantitativa de Hilbert variable asistida por aprendizaje profundo
Apprentissage profond assisté variationnel Hilbert Quantitative Phase Imaging
Количественная фазовая визуализация Гильберта
Zhuoshi Li 李卓识 ¹ ² ³, Jiasong Sun 孙佳嵩 ¹ ² ³, Yao Fan 范瑶 ¹ ² ³, Yanbo Jin 金彦伯 ¹ ² ³, Qian Shen 沈茜 ¹ ² ³, Maciej Trusiak ⁴, Maria Cywińska ⁴, Peng Gao 郜鹏 ⁵, Qian Chen 陈钱 ³, Chao Zuo 左超 ¹ ² ³
¹ Smart Computational Imaging Laboratory (SCILab), School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
中国 南京 南京理工大学智能计算成像实验室
² Smart Computational Imaging Research Institute (SCIRI) of Nanjing University of Science and Technology, Nanjing 210094, China
中国 南京 南京理工大学智能计算成像研究院
³ Jiangsu Key Laboratory of Spectral Imaging and Intelligent Sense, Nanjing 210094, China
中国 南京 江苏省光谱成像与智能感知重点实验室
⁴ Institute of Micromechanics and Photonics, Warsaw University of Technology, 8 Sw. A. Boboli St., Warsaw 02-525, Poland
⁵ School of Physics, Xidian University, Xi'an 710126, China
中国 西安 西安电子科技大学物理学院
We propose a high-accuracy artifacts-free single-frame digital holographic phase demodulation scheme for relatively low-carrier frequency holograms—deep learning assisted variational Hilbert quantitative phase imaging (DL-VHQPI). The method, incorporating a conventional deep neural network into a complete physical model utilizing the idea of residual compensation, reliably and robustly recovers the quantitative phase information of the test objects.
It can significantly alleviate spectrum-overlapping-caused phase artifacts under the slightly off-axis digital holographic system. Compared to the conventional end-to-end networks (without a physical model), the proposed method can reduce the dataset size dramatically while maintaining the imaging quality and model generalization.
The DL-VHQPI is quantitatively studied by numerical simulation. The live-cell experiment is designed to demonstrate the method's practicality in biological research. The proposed idea of the deep learning-assisted physical model might be extended to diverse computational imaging techniques.
