Year
Month
(Peer-Reviewed) Deep learning assisted variational Hilbert quantitative phase imaging
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
Opto-Electronic Science, 2023-05-18
Abstract

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.
Deep learning assisted variational Hilbert quantitative phase imaging_1
Deep learning assisted variational Hilbert quantitative phase imaging_2
Deep learning assisted variational Hilbert quantitative phase imaging_3
  • Deep-red and near-infrared organic lasers based on centrosymmetric molecules with excited-state intramolecular double proton transfer activity
  • Chang-Cun Yan, Zong-Lu Che, Wan-Ying Yang, Xue-Dong Wang, Liang-Sheng Liao
  • Opto-Electronic Advances
  • 2023-07-20
  • Encoding physics to learn reaction–diffusion processes
  • Chengping Rao, Pu Ren, Qi Wang, Oral Buyukozturk, Hao Sun, Yang Liu
  • Nature Machine Intelligence
  • 2023-07-17
  • Accurate medium-range global weather forecasting with 3D neural networks
  • Kaifeng Bi, Lingxi Xie, Hengheng Zhang, Xin Chen, Xiaotao Gu, Qi Tian
  • Nature
  • 2023-07-05
  • Highly sensitive and stable probe refractometer based on configurable plasmonic resonance with nano-modified fiber core
  • Jianying Jing, Kun Liu, Junfeng Jiang, Tianhua Xu, Shuang Wang, Tiegen Liu
  • Opto-Electronic Advances
  • 2023-06-25
  • In-flow holographic tomography boosts lipid droplet quantification
  • Michael John Fanous, Aydogan Ozcan
  • Opto-Electronic Advances
  • 2023-06-25
  • The second fusion of laser and aerospace—an inspiration for high energy lasers
  • Xiaojun Xu, Rui Wang, Zining Yang
  • Opto-Electronic Advances
  • 2023-06-25
  • Hot electron electrochemistry at silver activated by femtosecond laser pulses
  • Oskar Armbruster, Hannes Pöhl, Wolfgang Kautek
  • Opto-Electronic Advances
  • 2023-06-25
  • Highly sensitive microfiber ultrasound sensor for photoacoustic imaging
  • Perry Ping Shum, Gerd Keiser, Georges Humbert, Dora Juan Juan Hu, A. Ping Zhang, Lei Su
  • Opto-Electronic Advances
  • 2023-06-25
  • Integral imaging-based tabletop light field 3D display with large viewing angle
  • Yan Xing, Xing-Yu Lin, Lin-Bo Zhang, Yun-Peng Xia, Han-Le Zhang, Hong-Yu Cui, Shuang Li, Tong-Yu Wang, Hui Ren, Di Wang, Huan Deng, Qiong-Hua Wang
  • Opto-Electronic Advances
  • 2023-06-25
  • Microsphere femtosecond laser sub-50 nm structuring in far field via non-linear absorption
  • Zhenyuan Lin, Kuan Liu, Tun Cao, Minghui Hong
  • Opto-Electronic Advances
  • 2023-06-25



  • Hybrid bound states in the continuum in terahertz metasurfaces                                Top-down control of bottom-up material synthesis @ nanoscale
    About
    |
    Contact
    |
    Copyright © PubCard