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深度学习下的计算成像:现状、挑战与未来 (特邀综述)

Deep Learning Based Computational Imaging: Status, Challenges, and Future (Invited)

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摘要

近年来,光学成像技术已经由传统的强度、彩色成像发展进入计算光学成像时代。计算光学成像基于几何光学、波动光学等理论对场景目标经光学系统成像再到探测器采样这一完整图像生成过程建立精确的正向数学模型,再求解该正向成像模型所对应的“逆问题”,以计算重构的方式来获得场景目标的高质量图像或者传统技术无法直接获得的相位、光谱、偏振、光场、相干度、折射率、三维形貌等高维度物理信息。然而,计算成像系统的实际成像性能也同样极大程度地受限于“正向数学模型的准确性”以及“逆向重构算法的可靠性”,实际成像物理过程的不可预见性与高维病态逆问题求解的复杂性已成为这一领域进一步发展的瓶颈问题。近年来,人工智能与深度学习技术的飞跃式发展为计算光学成像技术开启了一扇全新的大门。不同于传统计算成像方法所依赖的物理驱动,深度学习下的计算成像是一类由数据驱动的方法,它不但解决了许多过去计算成像领域难以解决的难题,还在信息获取能力、成像的功能、核心性能指标(如成像空间分辨率、时间分辨率、灵敏度等)上都获得了显著提升。基于此,首先概括性介绍深度学习技术在计算光学成像领域的研究进展与最新成果,然后分析了当前深度学习技术在计算光学成像领域面临的主要问题与挑战,最后展望了该领域未来的发展方向与可能的研究方向。

Abstract

In recent years, optical imaging techniques have entered into the era of computational optical imaging from the traditional intensity and color imaging. Computational optical imaging, which is based on geometric optics, wave optics, and other theoretical foundations, establishes an accurate forward mathematical model for the whole image formation process of the scene imaged through the optical system and then sampled by the digital detector. Then, the high-quality reconstruction of the image and other high dimensional information, such as phase, spectrum, polarization, light field, coherence, refractive index, and three-dimension profile, which cannot be directly accessed using traditional methods, can be obtained through computational reconstruction method. However, the actual imaging performance of the computational imaging system is also limited by the “accuracy of the forward mathematical model” and “the reliability of inverse reconstruction algorithm”. Besides, the unpredictability of real physical imaging process and the complexity of solving high dimensional ill-posed inverse problems have become the bottleneck of further development of this field. In recent years, the rapid development of artificial intelligence and deep learning for the technology opens a new door for computational optical imaging technology. Unlike “physical driven” model that traditional computational imaging method is based on, computational imaging based on deep learning is a kind of “data-driven” method, which not only solves many problems considered quite challenge to be solved in this field, but also achieves remarkable improvement in information acquisition ability, imaging functions, and key performance indexes of imaging system, such as spatial resolution, temporal resolution, and detection sensitivity. This review first briefly introduces the current status and the latest progress of deep learning technology in the field of computational optical imaging. Then, the main problems and challenges faced by the current deep learning method in computational optical imaging field are discussed. Finally, the future developments and possible research directions of this field are prospected.

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补充资料

中图分类号:O436

DOI:10.3788/AOS202040.0111003

所属栏目:“计算光学成像"专题

基金项目:国家自然科学基金、总装“十三五”装备预研项目、总装“十三五”领域基金、国防科技项目基金、江苏省杰出青年基金、江苏省重点研发计划、江苏省“333工程”科研项目资助计划、江苏省光谱成像与智能感知重点实验室开放基金;

收稿日期:2019-11-06

修改稿日期:2019-12-05

网络出版日期:2020-01-01

作者单位    点击查看

左超:南京理工大学电子工程与光电技术学院,智能计算成像实验室(SCILab), 江苏 南京 210094南京理工大学江苏省光谱成像与智能感知重点实验室, 江苏 南京 210094
冯世杰:南京理工大学电子工程与光电技术学院,智能计算成像实验室(SCILab), 江苏 南京 210094南京理工大学江苏省光谱成像与智能感知重点实验室, 江苏 南京 210094
张翔宇:南京理工大学电子工程与光电技术学院,智能计算成像实验室(SCILab), 江苏 南京 210094南京理工大学江苏省光谱成像与智能感知重点实验室, 江苏 南京 210094
韩静:南京理工大学江苏省光谱成像与智能感知重点实验室, 江苏 南京 210094
陈钱:南京理工大学江苏省光谱成像与智能感知重点实验室, 江苏 南京 210094

联系人作者:陈钱(chenqian@njust.edu.cn)

备注:国家自然科学基金、总装“十三五”装备预研项目、总装“十三五”领域基金、国防科技项目基金、江苏省杰出青年基金、江苏省重点研发计划、江苏省“333工程”科研项目资助计划、江苏省光谱成像与智能感知重点实验室开放基金;

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引用该论文

Zuo Chao,Feng Shijie,Zhang Xiangyu,Han Jing,Qian Chen. Deep Learning Based Computational Imaging: Status, Challenges, and Future[J]. Acta Optica Sinica, 2020, 40(1): 0111003

左超,冯世杰,张翔宇,韩静,陈钱. 深度学习下的计算成像:现状、挑战与未来[J]. 光学学报, 2020, 40(1): 0111003

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