摘要
数字全息显微成像技术因能高精度实现定量相位成像的优势受到生物成像与材料科学领域的关注,但共轭像的存在、相位包裹的困扰以及分辨率受限等问题一直阻碍了数字全息显微术的广泛应用。近些年,深度学习作为机器学习中一种对数据特征提取进行特化的模型,在光学成像领域中被广泛应用。除用于提高成像效率外,其解决成像逆问题的潜力也不断被研究人员发掘,为成像领域开辟了一条蹊径。本文从深度学习应用于数字全息显微成像的工作原理出发,介绍它解决光学成像逆问题的思路与重要数理概念,同时对深度学习的完整实施过程进行归纳。扼要地总结了近年来深度学习对于全息重建、自动聚焦与相位恢复、全息去噪与超分辨等方面的研究进展,并对该研究领域中存在的问题与发展趋势进行展望。
Digital holographic microscopy(DHM)has attracted attention in the fields of biological imaging and materials science due to its advantages in high-precision quantitative phase imaging.However,the existence of conjugate images,the problem of phase wrapping,and the limited resolution have always hindered the wide application of DHM.In recent years,deep learning,as a specialized model for data feature extraction in machine learning,has been widely used in the field of optical imaging.In addition to improving imaging efficiency,its potential to solve imaging inverse problems has also been continuously explored by researchers,opening up a new path for the optical imaging.In this paper,we start from the working principle of deep learning applied to DHM,introduces its ideas and important mathematical concepts to solve the inverse problem of optical imaging,and at the same time summarizes the complete implementation process of deep learning.A brief summary of the research progress in recent years of deep learning in holographic reconstruction,auto-focusing and phase recovery,and holographic denoising and super-resolution is given,and summarize the existing problems in this research field and look forward to the development trend of research.
作者
孟章
丁浩
聂守平
马骏
袁操今
Meng Zhang;Ding Hao;Nie Shouping;Ma Jun;Yuan Caojin(Jiangsu Key Laboratory for Opto-Electronic Technology,School of Physics and Technology,Nanjing Normal University,Nanjing,Jiangsu 210023,China;School of Electronic Engineering and Optoelectronic Techniques,Nanjing University of Science and Technology,Nanjing,Jiangsu 210094,China)
出处
《激光与光电子学进展》
CSCD
北大核心
2021年第18期157-175,共19页
Laser & Optoelectronics Progress
基金
国家自然科学基金(61775097,61975081)
国家重点研发计划(2017YFB0503505)。
关键词
成像系统
数字全息显微
深度学习
定量相位成像
imaging systems
digital holographic microscopy
deep learning
quantitative phase imaging
