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上帝说要有光，于是便有了光；光学“optics”一词源自古希腊字“ὀπτική”，意为 “看见”、“视见”。三千年前，古埃及人与美索不达米亚人第一次将石英晶体磨光制成宁路德透镜（Nimrud lens），这翻开了人类光学成像历史的第一页[1]。时光流转，如今我们手持搭载潜望式长焦镜头与人工智能算法的智能手机就能拍摄皎洁白月与绚丽星空[2]。现如今，人类享受着光学成像技术带来的多姿多彩的绚丽生活，也一直在为了看得“更远、更广、更清晰”这个永无止境的目标前赴后继。由于视觉是人类获得客观世界信息的主要途径，据估计人类感知外界信息有80%是来自于视觉。而人眼由于受限于视觉性能，在时间、空间、灵敏度、光谱、分辨力等方面均存在局限性。光学成像技术利用各种光学成像系统，即获取客观景物图像的工具，如显微镜、望远镜、医疗CT、手机摄像机和照相机等（见图1），实现光信息的可视化，同时延伸并扩展人眼的视觉特性。
一个典型的光学成像系统主要由光源、光学镜头组、光探测器三部分组成。光学镜头将三维场景目标发出或者透/反/散射的光线聚焦在表面上，探测器像素和样品之间通过建立一种直接的一一对应关系来获取图像，光场的强度由光探测器离散采集并经过图像处理器数字化处理后形成计算机可显示的图像，整个过程如图2所示。这种“所见即所得”的成像方式受强度成像机理、探测器技术水平、光学系统设计、成像衍射极限等因素限制以及单视角、相位丢失、光谱积分、二维平面成像等因素的制约，导致高维度样品信息的缺失或丢失。此外光学镜头组通常需要和光学镜片、镜筒、光圈以及调焦系统等部件配合使用以获得清晰的图像，大大增加了成像装置的体积和复杂度。
图 1 常见的光电成像系统
Figure 1. Common optoelectronic imaging systems
图 2 传统光学成像系统的成像过程
Figure 2. Conventional optical imaging process
光学成像技术的出现延伸并扩展人眼的视觉特性，其以成像分辨率（时间、空间、光谱）的提高、成像维度的拓展、探测灵敏度的提升作为技术发展目标（图3）。受当今电子信息时代的影响，高性能、低成本、体积小、重量轻的光学成像系统越来越受到广泛的重视与需求。商用相机和手机摄像头因其光学系统结构小巧，价格低廉，已成为人们不可或缺的日常用品。然而传统光学成像系统因受强度成像机理、探测器技术水平、光学系统设计、成像衍射极限等因素制约，在空间分辨、时间分辨、光谱分辨、信息维度与探测灵敏度等方面仍存在一定局限性。随着人们对成像系统功能与性能的不断追求，以及军用和民用领域日益增长的高分辨、高灵敏度以及多维高速成像的应用需求，也对光学成像技术提出了更具挑战性的要求：例如在显微成像领域，一方面需要显微成像系统能够对无色透明的生物细胞组织实现无标记、多维度、高分辨、宽视场成像观察，另一方面需要显微成像系统能够小型化便携式，以满足当今迅速增长的即时检验与远程医疗的应用需求。在空间科技领域，同样需要光学成像系统不断减小重量和体积，以节省运载空间或降低运载成本。在工业制造领域，需要视觉检测仪要能够实现高精度、高分辨、高速实时的三维成像与传感，以满足快速在线检测与机器人视觉导航等应用需求。在医疗诊断领域，如内窥镜等设备，在保证清晰成像观测的同时，需要将设备做得更小，以减轻患者的痛苦与不适。在地质勘探领域，如在光线较暗的环境探测情况下，需要光学成像系统对光具有更高的透过率、响应灵敏度和动态范围，以提高图像的亮度与成像的信噪比。采用传统光学成像系统设计思路想要获得成像性能的少量提升，通常意味着硬件成本的急剧增加，甚至难以实现工程化应用。另一方面，光探测器规模尺寸、像元大小、响应灵敏度等已接近物理极限，很难满足这些极具挑战性的需求。
图 3 光学成像技术的五方面发展目标
Figure 3. Five goals for the development of optical imaging technology
随着成像电子学的发展，计算机数据处理能力的增强，光场调控、孔径编码、压缩感知、全息成像等光、电信息处理技术取得了重大的进展；另一方面，经过成千上万年，自然界已经演化出多类能够适应不同生存需求的生物视觉系统，从生物视觉系统中获得灵感无疑可以对新一代光学成像技术的发展带来有益的启示。在此背景下，20世纪90年代中期光学成像界和计算机视觉界的许多研究人员不约而同地探索出了一种新型成像模式：即图像形成不再仅仅依赖于光学物理器件，而是前端光学和后探测信号处理的联合设计[3]，这种技术就是现在广为人知的“计算成像”（Computational imaging）技术。计算成像将光学调控与信息处理有机结合，为突破上述传统成像系统中的诸多限制性因素提供了新手段与新思路[3]。对于“计算成像”，目前国际上并没有清晰的界定和严格的定义。目前普遍接受的一种说法是计算成像是通过光学系统和信号处理的有机结合与联合优化来实现特定的成像系统特性，它所得到的图像或信息是二者简单相加所不能达到的。它可以摆脱传统成像系统的限制，并且能够创造新颖的图像应用[4–8]。这种成像技术的实现方法与传统成像技术有着实质上的差别，给光学成像领域注入了新的活力[9]。21世纪初，计算成像技术在斯坦福大学、麻省理工学院、哥伦比亚大学、杜克大学、南加州大学、微软研究院等国际著名研究机构的研究学者的推动下得以迅猛发展，发展了波前编码成像、光场成像、时间编码成像、孔径编码成像、偏振成像、高光谱成像、单像素成像、结构光三维成像、数字全息成像、无透镜成像、定量相位成像、衍射层析成像、穿透散射介质成像等一系列计算光学成像的新概念与新体制。近年来，光学成像技术的发展已经由传统的强度、彩色成像发展进入计算光学成像时代。通过将光学系统的信息获取能力与计算机的信息处理能力相结合，实现相位、光谱、偏振、光场、相干度、折射率、三维形貌等高维度视觉信息的高性能、全方位采集。现如今，计算光学成像已发展为一门集几何光学、信息光学、计算光学、计算机视觉、现代信号处理等理论于一体的新兴交叉技术研究领域，成为光学成像领域的一大国际研究重点和热点。
这里必须说明的是：“计算成像”这个新兴词汇很容易被误解为“计算机成像”，或者仅仅被误认为是“传统成像”与“数字图像处理”技术的延伸。笔者认为这里有必要加以强调与区分。传统光学成像是为了获得可满足人眼或者机器视觉要求的图像，所以在进行图像采集时就需要保证获取高质量的图像数据。而实际操作中由于种种原因，成像效果往往达不到理想预期，所以通常还需要借助于数字图像处理技术对采集图像进行进一步加工。从学术级的Matlab、ImageJ，到专业级的Adobe Photoshop，乃至大众都在使用的“美图秀秀”，都属于典型的数字图像处理软件的范畴。在此过程中，光学成像过程与数字图像处理是独立且串行的关系，算法被认为是后处理过程，并不纳入成像系统设计的考虑之中，如图4所示。这即决定了传统成像技术无法从根本上通过图像处理技术来挖掘出更多场景的本质信息。简言之，如果成像前端所获取的图像数据缺失或者质量不理想（如严重离焦、噪声污染），后端仅依靠图像处理技术很难加以弥补。因为信息并不会凭空产生，正所谓“巧妇难为无米之炊”。
图 4 传统数字图像处理往往仅作为成像的后处理过程
Figure 4. Conventional digital imaging processing is only a post-processing step in the whole imaging process
与传统光学成像系统“先成像，后处理”的成像方式截然不同，计算光学成像采用的是“先调制，再拍摄，最后解调”的成像方式。其将光学系统（照明、光学器件、光探测器）与数字图像处理算法作为一个整体考虑，并在设计时一同进行综合优化。前端成像元件与后端数据处理二者相辅相成，构成一种“混合光学—数字计算成像系统”，如图5所示。不同于传统光学成像的“所见即所得”，计算光学成像通过对照明与成像系统人为引入可控的编码或者“扭曲”，如结构照明、孔径编码、附加光学传函、子孔径分割、探测器可控位移等并作为先验知识，目的是将物体或者场景更多的本质信息调制到传感器所能拍摄到的原始图像信号中（又被称作中间像，Intermediate image，因为该图像往往无法直接使用或观测）。在解调阶段，基于几何光学、波动光学等理论基础上通过对场景目标经光学系统成像再到探测器这一完整图像生成过程建立精确的正向数学模型，再经求解该正向成像模型的“逆问题”，以计算重构的方式来获得场景目标的高质量的图像或者所感兴趣的其它物理信息。正如其名，“计算成像”中的图像并不是直接拍摄到的，而是计算出来的。这种计算成像方法实质上就是在场景和图像之间建立了某种特定的联系，这种联系可以是线性的也可以是非线性的，可以突破一一对应的直接采样形式，实现非直接的采样形式，使得采样形式更加灵活，更能充分发挥不同传感器的特点与性能。如果说光电成像技术延伸并扩展了人眼的视觉特性，那么计算成像技术则进一步延伸并扩展光电成像器件的成像维度与探测性能。这种新型的成像方式将有望突破传统光学成像技术对光学系统以及探测器制造工艺、工作条件、功耗成本等因素的限制，使其在功能（相位、光谱、偏振、光场、相干度、折射率、三维形貌、景深延拓，模糊复原，数字重聚焦，改变观测视角）、性能（空间分辨、时间分辨、光谱分辨、信息维度与探测灵敏度）、可靠性、可维护性等方面获得显著提高，有助于实现成像设备的高性能、微型化、智能化。
图 5 计算光学成像系统的成像过程
Figure 5. Computational optical imaging process
近年来，计算光学成像也已逐步进入了我国从事光学成像、光学测量、光信息学以及计算机视觉领域科研人员的视野，在光学信息获取与处理领域占据了越来越重要的地位。2017年，在包括我们在内的计算成像领域的同行学者的一致建议下，国家自然科学基金委结合未来学科的发展方向和趋势首次将“计算成像”列入信息科学部四处F05学科代码下F0501光学信息获取、显示与处理研究方向，并作为一个独立的子方向（F050109），见图6（注：2021年基金委信息科学部优化学科布局，调整代码之后仅保留一级和二级代码，已不再设立三级代码）。近年来，以“计算光学成像”为议题的国际会议与专题研讨会在国内也逐步兴起，国内外各类学术期刊均争相推出了相关专刊与专栏，广大从业人员对此领域的兴趣与热情日益高涨，前沿热点研究方向，如相位成像、全息成像、光谱成像、偏振成像、三维成像、光场成像、超分辨成像、无透镜成像、单像素成像（鬼成像）、穿透散射介质成像等层出不穷。因此现阶段迫切需要对此蓬勃发展且充满前景的新领域进行梳理，归纳与总结，并基于此为我国相关领域研究人员在计算光学成像技术及其应用领域方面提供一些有益的参考。
图 6 从2017年修订后的国家自然科学基金委学科代码，其中“计算成像”被列入信息科学部四处下一个独立的子方向（F050109）
Figure 6. The revised discipline code of the National Natural Science Foundation of China in 2017. “Computational imaging” has been listed as an independent sub-direction of Information Science (F050109)
在此背景下，本文作为本期《红外与激光工程》——南京理工大学专刊“计算光学成像技术”专栏的首篇论文，概括性地综述了计算光学成像领域的历史沿革（何来）、发展现状（何处）、并展望其未来发展方向（何去）与其所依赖的核心赋能技术（何从）。在第一章中，我们首先简要回顾光学成像技术的历史以及计算光学成像的发展由来。计算光学成像被认为是人类从光化学成像时代、胶片成像时代、数码成像时代后的第四次成像革命。第二章将是文中的要点内容，我们将综述计算光学成像技术的发展现状。这里我们按照采用计算成像技术的“动机”或者说计算成像技术所带来的成效来将计算光学技术体系进行细分。并对每种成像技术或者方法的基本原理、发展现状、代表性成果以及典型应用进行了概述。第三章评估了计算光学成像的当前优势和劣势，以及未来发展面临的机会和威胁。第四章分析了计算成像未来发展所依赖的核心赋能技术。最后，我们在第五章中给出了文中的总结性评论。值得说明的是，由于本文涉及内容广泛且作者水平精力与文章篇幅所限，文中难免存在疏漏与错误之处，在此由衷期望读者不吝指正。
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光学成像诞生与发展是时代的必然产物。科学技术的进步、人们对长驻影像的渴望、对影像记录和信息传播的需求催生了光学成像技术的诞生；同时光学成像技术的诞生又反过来更进一步促进了科技的发展与人们的需求。光学成像技术并不是某一个人发明出来的，而是经过数代人共同努力的成果，它是适应社会需求的必然产物。在摄影术诞生后180余年的今天，摄像头已经成为我们日常生活不可分割的一部分：打开微信支付宝扫一扫支付，拍一张自拍发个朋友圈，拍一段宠物视频上传抖音，用淘宝拍照识别商品已经成为人们的生活常态。而你可曾知道历史上第一张照片曝光要长达8 h，而现如今手机标配摄像头，在屏幕上按下快门的那一瞬间一张清晰的照片就出炉了。光学成像技术是怎么演变到如今这个阶段的？有哪些人、公司和产品推动了演变的发生？演变带走了什么，留下了什么？带着这些疑问，我们回顾了一番光学成像技术的演变历程。
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早在公元前四百多年，中国哲学家墨子观察到小孔成像的现象，并记录在他的著作《墨子•经下》中，成为有史以来对小孔成像最早的研究和论著，为摄影的发明奠定了理论基础。墨子之后，古希腊哲学家亚里士多德和数学家欧几里得、春秋时期法家韩非子、西汉淮南王刘安、北宋科学家沈括等中外科学家都对针孔成像有颇多论述，针孔影像，已为察觉乃至运用，但只可观察，无法记录。在15~16世纪文艺复兴时期，欧洲出现了供绘画用的“成像暗箱”（Camera obscura），如图7所示（最初由意大利人阿贝尔第（Leon Batisti Alberti）研制）。由于暗箱的发明，很多历史的记载中从来没有系统学过绘画的人都“突然之间”摇身一变成了绘画天才，写实技巧骤然提升。在那个摄影技术尚未出现的时代，涌现出了大量能与“单反照片”媲美的杰作。
图 7 16世纪用于绘图的暗箱装置
Figure 7. Camera obscura box, 16th century
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1725年，德国纽伦堡阿道夫大学医学教授亨利其·舒尔茨（Heinrich Schulze）发现硝酸银溶液在光作用下会变黑，并于1727年发表论文《硝酸银与白垩混合物对光的作用》，论文讨论了硝酸银混合物在光作用下记录图案的功能，德国人称之为现代摄影的始祖。1793年法国发明家尼埃普斯（Joseph Nicéphore Nièpce）和他的兄弟一起开始了对感光材料的实验。1822~1824年期间，他实验发现把沥青涂在玻璃板和金属板上能够实现感光。1825年，他成功地利用可以感光的纸把铜版画上的影像制作成了一幅图片，由此诞生世界上第一张照片——《牵马少年》，如图8所示。这个以牵马的人为对象的图片虽然不是用照相机“照”出来的，但是这张图片预示着感光材料在实际运用方面迎来了一个新时代。然而在摄影技术诞生的初期，由于感光材料的灵敏度很低，拍摄一张照片往往需要曝光几个小时。
图 8 尼埃普斯使用的暗箱相机和所拍摄的《牵马少年》
Figure 8. The Camera Obscura box used by Joseph Nicephore Niépce and his photo “the man with a horse”
1825年，尼埃普斯委托法国光学仪器商人夏尔·雪弗莱（Charles Chevalier）为他的暗箱制作光学镜片。并于1826年（也有说1827年）将其发明的感光材料放进暗箱，拍摄现存最早的景物照片，作品《Le Gras窗外的景色》（见图9）在其法国勃艮第（Burgundy）的家里拍摄完成，通过其阁楼上的窗户拍摄，使用暗箱曝光时间超过8小时。尼埃普斯把这种用日光将影像永久记录在金属板上的摄影方法叫做“日光摄影法（Heliography）”-“Helios”来自希腊语.意即太阳，“Graphein”意即记录、描绘。
图 9 尼埃普斯所拍摄的《窗外景色》
Figure 9. “Window at Le Gras” taken by Joseph Nicephore Niépce
19世纪20年代，法国发明家、画家和舞台背景设计师路易·达盖尔（Louis-Jacques-Mandé Daguerre）开始热衷于寻找把暗箱投影固定下来的方法。他于1827年结识了尼埃普斯，两人于1829年12月开始了正式合作，订立了为期10年的合作契约，在尼埃普斯先前日光摄影法的基础上，共同研究和改进留住影像的工艺。1833年7月5日，尼埃普斯在没有取得丝毫新成果的情况下溘然辞世。但幸运的是，他的笔记留给继续工作的达盖尔。1837年的一天，达盖尔在药品箱中找药品时，突然看到过去曝过光的底片上的影像变得十分清晰。他猜想很可能是药箱里的某种药品在发生作用。为了找到答案，他每天晚
Computational optical imaging: An overview
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摘要: 计算光学成像是一种通过联合优化光学系统和信号处理以实现特定成像功能与特性的新兴研究领域。它并不是光学成像和数字图像处理的简单补充，而是前端（物理域）的光学调控与后端（数字域）信息处理的有机结合，通过对照明、成像系统进行光学编码与数学建模，以计算重构的方式获取图像与信息。这种新型的成像方式将有望突破传统光学成像技术对光学系统以及探测器制造工艺、工作条件、功耗成本等因素的限制，使其在功能（相位、光谱、偏振、光场、相干度、折射率、三维形貌、景深延拓、模糊复原、数字重聚焦，改变观测视角）、性能（空间分辨、时间分辨、光谱分辨、信息维度与探测灵敏度）、可靠性、可维护性等方面获得显著提高。现阶段，计算光学成像已发展为一门集几何光学、信息光学、计算光学、现代信号处理等理论于一体的新兴交叉技术研究领域，成为光学成像领域的国际研究重点和热点，代表了先进光学成像技术的未来发展方向。国内外众多高校与科研院所投身其中，使该领域全面进入了“百花齐放，百家争鸣”的繁荣发展局面。作为本期《红外与激光工程》——南京理工大学专刊“计算光学成像技术”专栏的首篇论文，本文概括性地综述了计算光学成像领域的历史沿革、发展现状、并展望其未来发展方向与所依赖的核心赋能技术，以求抛砖引玉。Abstract: Computational optical imaging is an emerging research field to realize specific imaging functions and characteristics by jointly optimizing optical systems and signal processing. It is not a simple supplement to optical imaging and digital image processing, but rather an integrally combination of optical modulation at the front end (physical domain) and information processing at the back end (digital domain), where images and information are obtained through optical coding and mathematical modeling of the illumination and imaging system in a computationally reconfigurable manner. This new imaging mechanism is expected to break the limitations of traditional optical imaging technology on the optical system and image detector fabrication, manufacturing, operating conditions, power consumption, and cost, and significantly improve imaging function (phase, spectrum, polarization, light field, coherence, refractive index, 3D morphology, depth of field, blur recovery, digital refocusing, change of view angle), performance (spatial resolution, temporal resolution, spectral resolution, information dimension, sensitivity), reliability, and maintainability. At present, computational optical imaging has been developed into an emerging interdisciplinary research field that integrates geometric optics, information optics, computer vision, digital image processing, modern signal processing, etc., and has become an international research focus and hotspot in the field of optical imaging, representing the future development direction of advanced optical imaging technology. Many universities and research institutes at home and abroad are getting involved, making it a rapidly developing research field where "a hundred flowers bloom and a hundred schools of thought contend". As the first article in the column "Computational optical imaging technology" of the special issue "Nanjing University of Science and Technology" for the Journal Infrared and Laser Engineering, this paper provides a general overview of historical evolution and development status of computational optical imaging, and looks forward to its future development direction and the core enabling technologies on which it relies, to throw bricks and attract jade.
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Key words:
- computational imaging /
- computational photography /
- optical imaging /
- optical sensing
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图 3 光学成像技术的五方面发展目标
Figure 3. Five goals for the development of optical imaging technology
图 4 传统数字图像处理往往仅作为成像的后处理过程
Figure 4. Conventional digital imaging processing is only a post-processing step in the whole imaging process
图 6 从2017年修订后的国家自然科学基金委学科代码，其中“计算成像”被列入信息科学部四处下一个独立的子方向（F050109）
Figure 6. The revised discipline code of the National Natural Science Foundation of China in 2017. “Computational imaging” has been listed as an independent sub-direction of Information Science (F050109)
图 8 尼埃普斯使用的暗箱相机和所拍摄的《牵马少年》
Figure 8. The Camera Obscura box used by Joseph Nicephore Niépce and his photo “the man with a horse”
图 10 1838年达盖尔所拍摄的《Boulevard du Temple》
Figure 10. “Boulevard du Temple” taken by Joseph Nicephore Niépce, 1838
图 11 塔尔博特所拍摄的“冬天里的橡树”（负片与正片）
Figure 11. William Henry Fox Talbot – An oak tree in winter (Negative and positive)
图 15 1888年在纽约刊登的柯达相机广告
Figure 15. The advertisement of Kodak camera in New York, 1888.
图 21 史蒂文·萨森研发出世界上第一部数码相机
Figure 21. The first digital camera developed by Steven Sasson
图 23 夏普联合J-Phone推出的全球首款拍照手机J-SH04
Figure 23. The first handphone with camera, J-SH04, developed by SHARP and J-Phone
图 24 采用了卡尔蔡司（Carl Zeiss）认证镜头的诺基亚N90
Figure 24. Nokia N90 handphone with Carl Zeiss optics
图 26 乔布斯在Macworld 2007大会上发布的第一代iPhone
Figure 26. Apple iPhone 1 released in the Macworld 2007 by Steve Jobs
图 27 2010年乔布斯推出苹果划时代的产品iPhone 4
Figure 27. Apple iPhone 4 released by Steve Jobs in 2010
图 28 全世界第一台双摄手机LG Optimus 3 D
Figure 28. The first dual camera mobile phone — LG Optimus 3 D
图 29 苹果在iPhone X上引入的结构光 3D人脸识别
Figure 29. Structured light 3D face recognition technique in iPhone X
图 30 华为P30 Pro及其在50倍的变焦下拍摄的月亮表面的细节清晰可见（虽然有争议说是AI修正合成的结果）
Figure 30. Huawei P30 Pro and captured moon with highly clear surface details (though it is controversial that it is the result of AI synthesis)
图 31 胶卷相机（Nikon F80）与数码相机（Nikon D50）的对比
Figure 31. Comparison of film camera (Nikon F80) with digital camera (Nikon D50)
图 32 最早的“计算成像”技术——合成孔径雷达
Figure 32. Synthetic Aperture Radar (SAR), the earliest computational imaging technique
图 33 最早的采用“计算成像”思想设计的光学成像系统——波前编码成像
Figure 33. Wave-front coding, the earliest optical imaging system involving the idea of computational imaging
图 34 3LCD投影仪与DLP投影仪的基本结构
Figure 34. Basic configurations of the 3LCD projector and the DLP projector
图 38 吴义仁研制的首个光场相机及其商业化产品
Figure 38. The first light field camera developed by Ng and its commercialized verison——Lytro
图 43 离轴数字全息重构基本原理
Figure 43. Principle of the off-axis digital holographic reconstruction
图 44 编码孔径成像基本原理与掩模板实物图
Figure 44. Principle of coded aperture imaging and a photograph of a coded mask
图 45 美国光学学会（OSA，现Optica）计算光学传感与成像国际会议议题
Figure 45. Topic categories of the Optica (formerly OSA) topic meeting COSI
图 46 Levoy教授所领导研发的Google Pixel相机多次登顶DXOMark榜单
Figure 46. Professor Levoy's Google Pixel camera tops DXOMark several times
图 47 “计算成像”、“计算光学”、“计算摄影”已经逐渐成为智能手机各大厂商的营销词汇
Figure 47. "Computational imaging", "computational optics" and "computational photography" have gradually become marketing terms for smartphone manufacturers
图 48 Facebook创始人Mark Zuckerberg宣布将Facebook更名为Meta并提出“元宇宙”概念。而三维传感技术有望将物理世界“数字化”，对于元宇宙的基建与落成都有着重大的实际意义
Figure 48. Facebook founder Mark Zuckerberg announced the renaming of Facebook as Meta and proposed the concept of "Metaverse", 3D sensing technology that promises to "digitize" the physical world and has great practical significance for the infrastructure and completion of Metaverse
图 49 基于“目的与动机”对典型计算光学成像技术所作的分类
Figure 49. Classification of typical computational imaging techniques according to their ''objectives and motivations''
图 51 Zernike相差显微术与微分干涉相差显微术
Figure 51. Zernike phase contrast microscopy and Differential Interference Contrast (DIC) microscopy
图 52 巨型迈克尔逊干涉仪——LIGO引力波探测器
Figure 52. Giant Michelson interferometer——LIGO wavefront detector
图 53 Shack-Hartmann波前传感器与四棱锥波前传感器
Figure 53. Schematics of Shack-Hartmann and pyramid wavefront sensors
图 56 晴天下游泳池底的光波图案。（池中水面的涟漪让阳光发生折射，在池底产生了明暗相间的网络结构）
Figure 56. The wave-like pattern at the bottom of a swimming pool in sunlight. (The pool surface refracts the incident sunlight to produces the characteristic pattern)
图 58 部分相干光场下的广义光强传输方程
Figure 58. Generalized transport of intensity equation (GTIE) for partially coherent field
图 60 基于弱相位近似的差分相衬定量相位成像原理示意图
Figure 60. Schematic diagram of the principle of quantitative phase imaging with DPC based on weak phase approximation
图 61 照明优化策略的对比。 (a) 对应的单次相位传递函数与合成相位传递函数；（b）最优照明下的各向同性定量相位成像结果
Figure 61. Comparison illumination-optimized schemes. (a) PTFs and their synthetic PTFs corresponding to different illumination functions; (b) Isotropic quantitative phase imaging results under optimal illumination
图 62 差分相衬定量相位成像的成像效率优化方案。（a）彩色复用三波段的差分相衬定量相位成像方案；(b) 基于三波长照明的多模态成像及定量相位成像方案；(c) 单帧差分相衬最优照明成像方案
Figure 62. Imaging efficiency optimization schemes of DPC. (a) Triple-wavelength multiplexed illumination scheme; (b) Triple-wavelength illumination scheme for multimodal imaging and DPC; (c) Single-shot optimal illumination scheme of DPC
图 65 画幅型层析成像光谱仪原理图
Figure 65. Schematic diagram of frame-type computer tomographic imaging spectrometer
图 69 同一场景的可见光、长波红外以及偏振成像结果
Figure 69. Visible light, long wave infrared and polarization imaging results for the same scene
图 70 基于旋转偏振片的偏振成像系统
Figure 70. Polarization imaging system based on rotating polarizer
图 75 大气散射模型与偏振去雾图像前后效果对比
Figure 75. Atmospheric scattering model and comparison of images before and after polarization defogging
图 76 牛心肌样品的三维PS-OCT成像结果。(a)三维整体结构图；(b)局部光轴图；(c)局部延迟图；(d)局部二向衰减图
Figure 76. PS-OCT imaging results of bovine myocardial samples. (a) 3D global structure map; (b) Local optical axis diagram; (c) Local delay map; (d) Local bi-direction attenuation map
图 77 对径压缩圆盘六步相移彩色光弹图像
Figure 77. Six-step phase-shifting color photoelastic images of the diametric compression disk
图 82 基于立体相位展开的四目实时三维测量系统及其测量结果。（a）笔者课题组提出的四目实时系统[450]；（b）该系统获取的动态场景下的实时彩色三维轮廓数据[450]；（c）该系统获取的全方位点云数据[456]；（d）该系统实现的360°三维面型缺陷检测[457]
Figure 82. Quad-camera real-time 3D measurement system based on stereo phase unwrapping and its measurement results. (a) Quad-camera real-time system proposed by our research group[450]; (b) The real-time color 3D data in the dynamic scene obtained by our system[451]; (c) The omnidirectional point cloud data obtained by our system[456]; (d) 360° 3D surface defect detection obtained by our system[457]
图 83 基于散斑相关法的商业产品。(a) Kinect; (b) PrimeSense; (c) iPhone X
Figure 83. Commercial products based on speckle correlation. (a) Kinect; (b) PrimeSense; (c) iPhone X
图 84 利用深度学习的单帧相位恢复方法流程图以及不同方法的三维重建结果。(a)基于深度学习相位恢复原理[460]；(b)不同条纹分析方法(FT、WFT、基于深度学习的方法和12步移相轮廓术)的三维重建比较[460]；(c)利用深度学习方法对一台不同转速的电扇进行了测量[462]；(d)利用深度学习单帧彩色条纹投影轮廓术对旋转工件的动态三维重建[464]；(e)利用深度学习单帧复合条纹投影轮廓术对旋转女孩模型的动态三维重建[465]
Figure 84. Flowchart of the single-frame phase retrieval approach using deep learning and the 3D reconstruction results of different approaches. (a) The principle of deep-learning-based phase retrieval method[460]; (b) Comparison of the 3D reconstructions of different fringe analysis approaches (FT, WFT, the deep-learning-based method, and 12-step phase-shifting profilometry) [460]; (c) The measurement results of a desk fan rotating at different speeds using our deep-learning method[462]; (d) The dynamic 3D measurement result of a rotating workpiece by deep-learning-based color FPP method[464]; (e) The dynamic 3D measurement result of a rotating bow girl model by composite fringe projection deep learning profilometry(CDLP)[465]
图 90 X 射线断层扫描技术。(a) X-ray 二维图像 与(b)三维CT 的对比及螺旋锥束扫描CT
Figure 90. X-ray computed tomography. (a) 2D X-ray image versus; (b) 3D X-ray CT and Spiral cone beam scanning CT
图 93 荧光显微镜拍摄到的细胞三维图像
Figure 93. An example of the acquired 3 D image of a cell, captured by a fluorescence microscope
图 94 理论计算得到的三维PSF的x-y和x-z平面切片图像。(a) x-y平面切片图像，每一切片上方数字表示该切片沿z轴方向距离点扩散函数中心亮点的距离；(b) x-z平面切片图像，每一切片上方数字表示该切片沿y轴方向距离点扩散函数中心亮点的距离
Figure 94. x-y and x-z slice images of three-dimensional PSF are calculated theoretically. (a) x-y slice images. The number above each slice represent the distance between the slice along the z-axis direction and the central highlights of the point spread function; (b) x-z slice images. The number above each slice indicates the distance between the slice along the y-axis and the highlight of the point spread function center.
图 95 反卷积三维荧光显微成像的工作流程
Figure 95. Workflow of deconvolution three-dimensional fluorescence microscopic imaging
图 96 光场显微镜模型[516]。(a)传统明场显微镜；(b)光场显微镜[516]；(c)基于波动光学的光场显微模型[518]；(d)傅里叶光场显微镜模型[519]
Figure 96. Model of Light field microscope[516]. (a) Traditional bright field microscope; (b) Light field microscopy[516]; (c) Light field microscopic based on wave optics theory[518]; (d) Fourier light field microscopy[519]
图 97 光场显微在生物科学中的应用。(a)小鼠头戴MiniLFM[522]；(b)使用HR-LFM成像COS-7活细胞中的高尔基源膜泡[523]；(c) DAOSLIMIT观测小鼠肝脏中中性粒细胞迁移过程中的迁移[525]；(d)共聚焦光场显微镜，观测斑马鱼的捕猎活动以及探测小鼠大脑的神经活动[525]
Figure 97. Light field applications in biological science. (a) Mouse with a head-mounted MiniLFM [522]; (b) Imaging Golgi-derived membrane vesicles in living COS-7 cells using HR-LFM [523]; (c) Migrasome dynamics during neutrophil migration in mouse liver with DAOSLIMIT[525]; (d) Confocal light field microscopy, tracking and imaging whole-brain neural activity during larval zebrafish’s prey capture behavior and imaging and tracking of circulating blood cells in awake mouse brain[524]
图 98 全息衍射层析显微术的代表性工作。（a）瑞士洛桑联邦理工学院Charriere等[532]的旋转物体测量；（b）美国麻省理工学院的Choi等[534]的扫描振镜测量；（c）瑞士洛桑联邦理工学院的Cotte等[537]楔形棱镜扫描；（d）韩国技术科学院的Park团队[547]的DMD扫描测量
Figure 98. Representative work on holographic diffraction tomography microscopy. （a） Rotating object measurements by Charriere et al[532]; （b） Scanning galvanometer measurements by Choi et al[534]; （c） Wedge prism scanning by Cotte et al[537]; （d） Park's team[547] for DMD scanning measurements
图 99 相位恢复衍射层析显微术的代表性工作。（a）澳大利亚墨尔本大学X衍射成像研究团队的Barty等[539]的显微镜平台旋转物体测量；（b）加州大学洛杉矶分校的Ozcan课题组[548]的无透镜片上层析平台；（c）笔者课题组[239]的基于LED阵列的无透镜平台；（d）笔者课题组[240]的基于LED阵列的显微镜平台
Figure 99. Representative work on phase retrieval diffraction tomography microscopy. （a） Microscope platform rotating object measurements by Barty et al[539] from the X diffraction imaging research team at the University of Melbourne, Australia; （b） Lens-free on-chip chromatography platform by the Ozcan group at UCLA[548]; （c） Lens-free LED array-based platform by our group[239]; （d） LED array-based microscopy platform of our group[240]
图 100 光强衍射层析显微术的两种实现方式。(a)基于轴向扫描的“光强传输衍射层析”（TIDT）显微术；(b)基于照明角度扫描的“傅里叶叠层衍射层析”（FPDT）显微术
Figure 100. Two implementations of optical intensity diffraction tomography. (a) TIDT microscopy based on axial scanning; (b) FPDT microscopy based on illumination angle scanning
图 101 光强传输衍射层析术的代表性工作。(a)笔者课题组[238]的基于高数值孔径环形照明的定量相位成像；(b) 西班牙马德里大学Alieva课题组[549]的电控变焦透镜的光强传输衍射层析；(c)笔者课题组[241]的基于环形照明的多孔径光强传输衍射层析
Figure 101. Representative work on TIDT. （a） Quantitative phase imaging based on high numerical aperture ring illumination by our group[238]; （b） TIDT with electronically controlled zoom lens by Alieva's group[549] at the University of Madrid, Spain; （c） Multi-aperture optical intensity transfer diffraction tomography based on ring illumination by our group[241]
图 102 傅里叶叠层衍射层析术的代表性工作。（a）美国加州大学伯克利分校的Waller课题组[550]的基于多层模型的傅里叶叠层三维成像；（b）美国加州理工学院的Yang课题组[185]的傅里叶叠层层析技术（一阶Born近似下不含暗场强度图）；（c）笔者课题组[186]的傅里叶叠层衍射层析成像技术（一阶Rytov近似下含暗场强度图）
Figure 102. Representative work on FPDT. （a） FPDT 3D imaging based on a multilayer model by Waller's group at UC Berkeley[550]; （b） FPDT without dark field intensity under the first-order Born approximation by Yang's group at Caltech[185]; （c） FPDT with dark field intensity under the first-order Rytov approximation by our group[186]
图 103 通过干涉测量法进行相干测量。(a)杨氏双缝干涉仪[561]；(b)逆波前杨氏干涉仪[562]；(c)非冗余孔径阵列[563]；(d)自参考干涉法[565]；(e)两点干涉仪；(f) Sagnac干涉仪[565-566]
Figure 103. Coherent measurement using interferometer. (a) Young’s interferometer[561]; (b) Reversed-wavefront Young interferometer[562]; (c) Non-redundant array[563]; (d) Self-referencing interferometer[565]; (e) Two-point interferometer; (f) Sagnac interferometer[565-566]
图 104 相空间断层扫描的原理与光路图。(a)原理示意图；(b)相空间断层扫描的光路结构，实验系统采用一对柱状透镜，在轴向z0处测量光强
Figure 104. The principal and optical setup of phase-space tomography. (a) Principle of phase space tomography; (b) A pair of cylindrical lenses oriented perpendicularly are used to introduce astigmatism to the measurement. Intensities are measured at planes with axial coordinate z0
图 106 两类成像分辨率对最终图像清晰度的影响。(a) 理想高分辨率图像；(b) 对于小视场的制导系统而言，成像系统的分辨率最终由光学分辨率，即成像系统的口径所决定（如图(c)所示），而对于大部分宽视场的搜索/跟踪系统而言，成像系统的分辨率最终由图像分辨率，即探测器的像素尺寸决定（如图(d)所示）
Figure 106. The influence of two kinds of imaging resolution on the final image definition. (a) ideal high resolution image; (b) for the guidance system with small field of view, the resolution of the imaging system is finally determined by the optical resolution, that is, the aperture of the imaging system (as shown in Figure(c)), while for most search / tracking systems with wide field of view, the resolution of the imaging system is finally determined by the image resolution, that is, the pixel size of the detector (as shown in Figure(d))
图 107 光学系统口径所限制的衍射分辨极限（艾里斑）。(a) 成像系统的最小可分辨距离（光学角分辨率）与成像系统的孔径成反比；(b)~(d) 两个非相干的点目标在不同间距下所能拍摄到的艾里斑图像
Figure 107. Diffraction resolution limit limited by the aperture of optical system (Airy spot). (a) The minimum resolvable distance (optical angular resolution) of the imaging system is inversely proportional to the aperture of the imaging system; (b)-(d) Airy spot images of two incoherent point targets at different distances
图 108 探测器像元大小所限制的奈奎斯特采样极限（马赛克效应）。(a) 像素采样不足（像素尺寸过大）所导致的信息混叠现象；(b) 恰好满足奈奎斯特采样极限时的情况; (c) 一个典型的红外热像仪对于人体目标在不同距离下的成像效果（像元尺寸为38 μm，像素为320×240，50 mm焦距镜头）
Figure 108. Nyquist sampling limit limited by detector pixel size (mosaic effect). (a) Information aliasing caused by insufficient pixel sampling (excessive pixel size); (b) When the Nyquist sampling limit is exactly met; (c) The imaging effect of a typical infrared thermal imager for human targets at different distances (Pixel size: 38 μm. 320× 240 pixels, 50 mm focal length lens)
图 109 像素超分辨重建的基本原理(逆向病态问题的最优解)
Figure 109. Basic principle of pixel super-resolution reconstruction (Optimal solution of inverse ill-posed problem)
图 111 被动亚像素移动超分辨成像基本原理
Figure 111. Basic principle of passive subpixel moving super-resolution imaging
图 112 可控亚像素移动所引起的像素级光强变化
Figure 112. Pixel level light intensity change caused by controllable sub-pixel movement
图 113 微扫描装置。(a)光学折射法；(b)平板旋转法；(c) 压电陶瓷体
Figure 113. Micro scanning device. (a) Optical refraction method; (b) Plate rotation method; (c) Piezoelectric ceramics body
图 116 (a) 研制的可见光波段孔径编码原理样机及成像结果；(b) 研制的红外波段孔径编码原理样机及成像结果
Figure 116. (a) Visible coded aperture imaging system and its reconstruction results; (b) Infrared coded aperture imaging system and its reconstruction results
图 118 (a) 美国Aerospace公司研制的基于光纤的激光合成孔径雷达成像原理图；(b) 成像结果对比（右图为衍射受限成像结果，左图为合成孔径后的结果图）
Figure 118. (a) Principle diagram of laser synthetic aperture radar imaging based on optical fibers developed by Aerospace Corporation of the United States; (b) Comparison of imaging results (right image is diffraction-limited imaging results, left image is synthetic aperture results)
图 119 非干涉傅里叶叠层成像合成孔径技术系统原理示意图
Figure 119. Schematic of non-interferometric synthetic aperture imaging technology based on Fourier ptychography
图 121 传统非相干合成孔径系统结构。(a)迈克尔逊型干涉仪；(b)中次镜结构；(c)相控阵列结构
Figure 121. Conventional incoherent synthetic aperture structure. (a) Michelson interferometer; (b) Common secondary structure; (c) Phased array structure
图 122 初代SPIDER成像概念系统设计模型。 (a) SPIDER设计模型和分解图；(b)两个物理基线和三个光谱波段的PIC示意图；(c) SPIDER微透镜排列方式；(d)对应排列方式下频谱覆盖
Figure 122. Design model of the initial generation of SPIDER imaging conceptual system. (a) Design model and explosive view of SPIDER; (b) PIC schematics of the two physical baselines and three spectral bands; (c) Arrangement of SPIDER lenslets; (d) Corresponding spatial frequency coverage
图 128 两种代表性的主动式超快光学成像技术。(a)Nakagawa等人提出的一种基于顺序时间全光映射摄影术的超快成像技术(sequentially time all-optical mapping photography, STAMP)[647]； (b)Kristensson等人提出的一种基于多曝光频率识别算法的超快成像技术(frequency recognition algorithm for multiple exposures, FRAME)[649]
Figure 128. Two representative active ultrafast optical imaging techniques. (a) An ultrafast imaging technique based on sequential time all-optical mapping photography (STAMP) proposed by Nakagawa et al.[647]; (b) An ultrafast imaging technique based on frequency recognition algorithm for multiple exposures (FRAME) proposed by Kristensson et al.[649]
图 130 基于数字光处理DLP（Digital Light Processing）技术的数字投影仪基本结构及其核心部件DMD
Figure 130. Basic structure of a digital projector based on Digital Light Processing (DLP) technology and its core component DMD
图 132 DMD显示8位灰度图像的二元时间脉冲宽度调制机理
Figure 132. Binary time pulse width modulation mechanism for 8-bit grayscale image displayed by DMD
图 134 对气枪发射的子弹的三维测量与跟踪[669]。(a)不同时间点的相机图像; (b)相应3D重建结果; (c)枪口区域的3D重建(对应于(b)中所示的盒装区域)以及在飞行过程中的三个不同时间点的子弹（7.5 ms，12.6 ms和17.7 ms）的3 D重建 (插图显示在17.7 ms处穿过飞行子弹中心的水平（x-z）和垂直（y-z）轮廓)；（d）最后时刻（135 ms）场景的3 D点云，彩色线显示130 ms长的子弹轨迹(插图为子弹速度-时间的图)
Figure 134. 3D measurement and tracking a bullet fired from a toy gun[669]. (a) Representative camera images at different time points; (b) Corresponding color-coded 3D reconstructions; (c) 3D reconstruction of the muzzle region (corresponding to the boxed region shown in (b)) as well as the bullet at three different points of time over the course of flight (7.5 ms, 12.6 ms, and 17.7 ms) (The insets show the horizontal (x–z) and vertical (y-z) profiles crossing the body center of the flying bullet at 17.7 ms); (d) 3D point cloud of the scene at the last moment (135 ms), with the colored line showing the 130 ms long bullet trajectory (The inset plots the bullet velocity as a function of time)
图 135 阵列投影技术及GOBO投影技术[678-679]。(a)阵列投影仪及用该投影仪搭建的三维测量系统；(b)GOBO投影仪及用该投影仪搭建的三维测量系统
Figure 135. Array projection technology and GOBO projection technology[678-679]. (a) Array projector and three-dimensional measuring system set up with the projector; (b) GOBO projector and three-dimensional measuring system set up with the projector
图 137 5D高光谱成像系统、结果及高速热成像系统、结果[680-681]。(a) 5D高光谱成像系统；(b) 高速热成像系统；(c) 5D高光谱成像结果：对柑橘植物的吸水性的测量；(d)高速热成像结果：不同时间对篮球运动员的测量
Figure 137. The systems and results of 5D hyperspectral imaging and high speed thermal imaging[680-681]. (a) 5D hyperspectral imaging system; (b) High speed thermal imaging system; (c) 5D hyperspectral imaging results: The measurement of water absorption by a citrus plant; (d) High-speed thermal imaging results: The measurement of a basketball player at different times
图 138 μDLP对高速转动风扇的三维测量[682]，这些场景在训练过程中都不存在。第一行至第三行为通过μDLP获得风扇在1000~5000 r/min相应的3D重建
Figure 138. Measurement of a dynamic scene that includes a static model and a falling table tennis[682], which are also not present in the training process. The first line to the third line pass µDLP obtains the corresponding 3D reconstruction of the fan at 1000 ~ 5000 r/min
图 139 像增强器工作原理及成像示意图
Figure 139. Working principle and imaging diagram of image intensifier
图 140 远距离成像情况下，EMCCD成像结果与单光子四种不同算法重建结果对比图
Figure 140. EMCCD imaging result is compared with the reconstruction results of four different single photon algorithms in the case of long-distance imaging
图 142 不同情况下的回波示意图与重建结果
Figure 142. Schematic diagram of echo and reconstruction results under different conditions
图 143 8.2 km外目标的超分辨成像结果
Figure 143. Super-resolution results of target located at 8.2 km
图 144 远距离单光子激光雷达成像示意图
Figure 144. Illustration of long range single photon Lidar imaging
图 145 首达光子的三维重建结果。(a)~(c) 单光子结果三个方向的逐点的最大似然处理；(d)~(e) 对应反射率估算的结果；(g)~(i) 环境噪声处理；(j)~(l) 结果的3D估计
Figure 145. Calculate the first photon 3D reconstruction of reflectance. (a)-(c) Point-by-point maximum likelihood processing in the three directions of the single photon result; (d)-(f) Corresponding reflectance estimation results; (g)-(i) Environmental noise processing; (j)-(l) 3D estimation results
图 146 超过 201.5 km的远程主动成像示意图。在中国乌鲁木齐市附近实施的实验的卫星图像，单光子激光雷达被放置在野外的一个临时实验室。(a) 由配备望远镜的标准天文相机拍摄的山脉可见波段照片，海拔约4500 m；(b) 实验装置示意图；(c) 设置硬件的照片，包括光学系统（左上角和左下角）和电子控制系统（右下角）；(d) 临时实验室在海拔 1770 m 处的视图
Figure 146. Illustration of the long-range active imaging over 201.5 km. Satellite image of the experiment implemented near the city of Urumqi, China, where the single-photon lidar is placed at a temporary laboratory in the wild. (a) Visible-band photograph of the mountains taken by a standard astronomical camera equipped with a telescope. The elevation is approximately 4500 m; (b) Schematic diagram of the experimental setup; (c) Photograph of the setup hardware, including the optical system (top and bottom left) and the electronic control system (bottom right; (d) View of the temporary laboratory at an altitude of 1770 m
图 147 201.5 km以上场景重建结果。(a)真实可见光照片；(b) Lindell等人在2018年对SBR ~ 0.04、平均信号PPP ~ 3.58的数据的重建深度结果；(c)重建结果的三维剖面图
Figure 147. Reconstruction results of a scene over 201.5 km. (a) Real visible-band photo; (b) The reconstructed depth result by Lindell et al. in 2018 for the data with SBR ~ 0.04 and mean signal PPP ~ 3.58; (c) A 3 D profile of the reconstructed result
图 149 提出的单光子三维成像多尺度网络图
Figure 149. Diagram of proposed multi-scale network for single-photon 3D imaging with multiple returns
图 150 三个远程户外场景的重建结果。第一行的高层建筑距离成像系统21.6 km，空间分辨率256×256，信噪比为0.114，每像素1.228光子。第二行距离成像系统1.2 km，空间分辨率176×176，信噪比为0.109，每像素3.957光子。第三行的高塔，距离成像系统3.8 km，空间分辨率512×512，信噪比为0.336，每像素1.371光子。GT表示系统在较长的采集时间内捕获的地面真实深度图
Figure 150. The reconstruction results for three long range outdoor scenes. First row: A tall building, that locates at 21.6 km away from imaging system with a spatial resolution of 256×256, signal-to-noise ratio is 0.114, and 1.228 photons per pixel. Second row: That locates at 1.2 km away from our imaging system with a spatial resolution of 176×176, signal-to-noise ratio is 0.109, and 3.957 photons per pixel. Third row: A tall tower named Pole, that locates at 3.8 km away from our imaging system with a spatial resolution of 512×512, signal-to-noise ratio is 0.336, and 1.371 photons per pixel. GT denotes the ground truth depth maps captured by system with a long acquisition time
图 151 对于传统光学系统，视场与分辨率这两个参数互相矛盾，无法同时兼顾。(a) 35 mm单反相机不同焦距下所对应的视场角；(b) 35 mm单反相机不同焦距下所拍摄到的典型图像
Figure 151. For traditional optical systems, the two parameters of field of view and resolution are contradictory and cannot be taken into account at the same time. (a) The corresponding field of view angle of 35 mm SLR camera under different focal length; (b) Typical images taken by 35 mm SLR camera under different focal lengths
图 152 GigaPan全景拍摄系统及拍摄拼接所得的像素全景图
Figure 152. GigaPan panoramic shooting system and pixel panorama obtained by shooting splicing
图 153 ARGUS-IS系统及其成像效果。（a） ARGUS-IS 系统外型；（b）系统采用了368个图像传感器和四个主镜头，其中92个传感器为一组，共用一个主镜头。通过巧妙设置传感器的安装位置，使得每组传感器获得的图像错位，互为补充，再通过图像拼接，能够得到较好的整体成像结果；（c）此成像系统在 6000 m高空有效覆盖7.2 km×7.2 km的地面区域
Figure 153. ARGUS-IS system and its imaging effect. (a) ARGUS-IS system appearance; (b) The system uses 368 image sensors and four main lenses, of which 92 sensors are a group and share a main lens. By skillfully setting the installation position of sensors, the images obtained by each group of sensors are misaligned and complementary to each other, and then through image mosaic, better overall imaging results can be obtained; (c) The imaging system effectively covers 7.2 km × 7.2 km ground area at an altitude of 6 km
图 154 多相机拼接系统。(a) Lytro公司所研制的光场采集系统Immerge；(b) 斯坦福半环型相机阵列系统；(c) 斯坦福平面型相机阵列系统；(d) Camatrix环型相机阵列系统；(e) 清华大学鸟笼相机阵列系统
Figure 154. Multi camera splicing system. (a) Light field acquisition system Immerge developed by lytro company; (b) Stanford semi ring camera array system; (c) Stanford planar camera array system; (d) Camatrix ring camera array system; (e) Tsinghua University birdcage camera array system
图 155 (a) 瑞士洛桑联邦理工学院（EPFL）的科研团队设计并研制了仿生复眼成像设备Panoptic；(b) 大视场高分辨率的OMNI-R系统；(c) Nicholas Law研制的艾弗里地基望远系统Evryscope
Figure 155. (a) The research team of the Federal Institute of Technology (EPFL) in Lausanne, Switzerland, designed and developed the bionic compound eye imaging device Panoptic; (b) OMNI-R system with large field of view and high resolution; (c) Everyscope, avery ground-based telescope system developed by Nicholas Law
图 156 多尺度成像系统。(a) AWARE-2结构图；(b) AWARE-10结构图；(c) AWARE-40结构图
Figure 156. Multiscale imaging system. (a) AWARE-2 structure drawing; (b) AWARE-10 structural drawing; (c) AWARE-40 structure drawing
图 157 传统显微镜存在分辨率与视场大小难以同时兼顾的矛盾：低倍镜下视野大，但分辨率低；切换到高倍镜后分辨率虽得以提升，视场却相应的成更高比例的缩减
Figure 157. There is a tradeoff between the resolution and FOV in traditional microscopes: The FOV under low-magnification objective is large with the low resolution; for high-magnification objective, the resolution is improved while the FOV is reduced dramatically
图 158 克服传统显微镜空间带宽积受限四类可能的解决方案。(a) 芯片上无透镜全息显微成像技术；(b) 傅里叶叠层显微成像技术；(c) 合成孔径/合成视场全息显微技术；(d) 基于流式细胞术的显微成像技术
Figure 158. Four types of possible solutions to overcome the limited spatial bandwidth area of conventional microscopes. (a) On-chip lens-free holographic microscopy; (b) Fourier ptychography microscopy; (c) Synthetic aperture/FOV holographic microscopy; (d) Flow cytometric microscopy
图 159 芯片上无透镜全息显微成像的“亚像素”超分辨技术。(a) 通过移动照明实现亚像素微扫描；(b) 笔者课题组所提出的基于倾斜平行平板的主动亚像素微扫描方案
Figure 159. Sub-pixel super-resolution technology based on the lens-free holographic microscope. (a) Sub-pixel micro-scanning by moving illumination; (b) Active sub-pixel micro-scanning scheme with inclined parallel plate proposed by our research group
图 161 基于单帧傅里叶叠层显微成像的高通量定量显微成像
Figure 161. High throughput quantitative microscopic imaging based on single frame Fourier ptychographic microscopy
图 166 图像立方法的概述[824]。(a)从场景中背向散射的照明激光脉冲的原始信号；(b)展宽后的信号；(c)利用探测信号得到的一组包含不同深度图像的立方；(d)横截面上的每个位置沿纵向轴的强度分布，包含深度信息；(e)反射率图；(f)深度图可以从图像立方体中解算出来，然后用于重建；(g)场景的三维图像
Figure 166. Overview of the image cube method[824]. (a) The illuminating laser pulses back-scattered from a scene are measured as (b) broadened signals; (c) An image cube, containing images at different depths, is obtained using the measured signals; (d) Each transverse location has an intensity distribution along the longitudinal axis, indicating depth information; (e) Reflectivity and (f) a depth map can be estimated from the image cube, and then be used to reconstruct; (g) A 3D image of the scene
图 167 多维傅里叶单像素成像实验结果[832]。（a）包含目标物体三个模态（空间-三维-彩色）的傅里叶谱，采样率为12%；（b）对重建效果图选中部分区域(红色实线部分)；（c）三维彩色重建上视图；（d）三维彩色重建斜视图；（e）三维彩色重建侧视图
Figure 167. Experimental results of multi-modality Fourier single-pixel imaging[832]. (a) Fourier transform with spatial, 3D, and color three modality information of target object, where sampling ratio = 12%; (b) Image reconstructed from (a) with partial enlargement; (c)-(e) Top, perspective, and side views of the three-dimensional reconstruction of the object
图 169 (a) 投影式无透镜显微成像实验系统图[833-835]；(b) 无透镜光流体显微镜横截面示意图；(c) (b) 中实验装置顶视图。白色圆圈为小孔，浅灰色虚线网格为镀铝二维CMOS图像传感器，蓝线为微流体通道[758,836]
Figure 169. (a) Experimental setup[833-835] for lens-free shadow imaging platform; (b) Cross-sectional scheme of the opToFluidic microscopy; (c) The top view of the device (b) The white circles are apertures. The gray dashed grid is the CMOS sensor coated with Al, and the blue lines are the whole microfluidic channel[758,836]
图 170 无透镜荧光成像原理图。整个成像系统放大率~1，全反射发生在玻璃-空气界面，位于玻璃层底部。为避免检测到散射激发光，在玻璃层下添加了塑料吸收滤光片。（TIR：全反射的缩写；图像是根据参考文献[837-838,841]修改而来）
Figure 170. Schematic diagram of the lens-free on-chip fluorescent imaging platform, whose platform has unit magnification. The TIR occurs at the glass-air interface at the bottom facet of the cover glass. To avoid detection of scattered photons a plastic absorption filter is used behind the faceplate. （TIR: Short for total reflection; The image was modified from the references[837-839,841]）
图 172 基于多角度照明的无透镜三维层析成像结果。(a) 马蛔虫子宫切片的折射率重构结果；(b) (a)中红色矩形框中折射率的三维渲染[239]；(c) 线虫在z=3μm位置的成像结果；(d1)~(d2) 分别为线虫的前部和后部在y-z平面的成像结果；(e1)~(e2) 分别为沿着(c)中实心箭头和虚线箭头方向的x-z平面的成像结果[548]。（图像是根据参考文献[239,548]修改而来）
Figure 172. 3D tomographic reconstructions of lens-free on-chip microscope based on multi-angle illumination. (a) The recovered refractive index depth sections of a slice of the uterus of Parascaris equorum; (b) The 3D renderings of the refractive index for the boxed area in (a)[239]; (c) A tomogram for the entire worm corresponding to a plane that is 3 μm above the center of the worm; (d1)-(d2) y-z ortho slices from the anterior and posterior regions of the worm, respectively; (e1)-(e2) x-z ortho slices along the direction of the solid and dashed arrow in (c), respectively[548]
图 174 基于菲涅尔波带片的非相干无透镜摄像。(a) 无镜头相机的实时图像捕获和重建[853]；(b) 利用菲涅尔波带片单帧无透镜相机对二值、灰度和彩色图像进行重建[856]
Figure 174. Lens-free imaging with FZP and incoherent illumination. (a) Real-time image capturing and reconstruction demonstration of a prototyped lens-free camera[853]; (b) the reconstructions for the binary, grayscale and color images using the FZP single-shot lens-free camera[856]
图 175 FlatCam结构。(a) 一个二进制的编码掩码被放置在离一个现成的数字图像传感器0.5 mm的地方，对场景进行编码；(b) 一个通过传感器测量和图像重建解决一个计算逆问题的例子
Figure 175. FlatCam architecture. (a) A binary, coded mask is placed 0.5 mm away from an off-the-shelf digital image sensor; (b) An example of sensor measurements and the image reconstructed by solving a computational inverse problem
图 176 自适应光学系统的成像原理图
Figure 176. Imaging principle of the system based on adaptive optics
图 178 成像和人眼测试所用的自适应光学系统的基本布局
Figure 178. Basic layout of an adaptive optics system for imaging and vision testing
图 179 AO-CSLO拍摄的人眼视网膜分层高分辨力图像。(a) 活体人眼视网膜感光细胞层；(b) 毛细血管层；(c) 神经纤维层图像
Figure 179. Layered high resolution images taken by the AO-CSLO system. (a) Layer of human retina photoreceptors in vivo; (b) Layer of blood capillaries; (c) Layer of nerve fibers
图 180 自适应光学在宽场荧光与共聚焦显微镜中的应用
Figure 180. Application of adaptive optics in wide field fluorescence and confocal microscope
图 181 自适应光学在共聚焦显微镜与多光子显微镜中的应用
Figure 181. Application of adaptive optics in confocal microscope and multiphoton microscope
图 182 自适应光学在宽场荧光显微镜和超分辨率荧光显微镜中的应用。 (a) 微管蛋白染色的 HeLa 细胞在校正之前(左) 和之后(右) 的宽场荧光显微成像[892]；(b) 一组标称直径为 121 nm 的荧光微球通过常规、共焦和结构光显微成像[894]；(c) 使用 DM 和 SLM 来补偿 STED显微镜中所有三个路径的像差[897]； (d) Atto647N 标记的囊泡谷氨酸转运蛋白在完整果蝇大脑突触中的共聚焦(左) 和 3D STED(右) 图像的比较[895]
Figure 182. Application of adaptive optics in wide field fluorescence microscopy and super-resolution fluorescence microscopy. (a) Wide-field fluorescence microscopy of tubulin stained HeLa cells before(left) and after(right) correction[892]; (b) A cluster of fluorescent microspheres of nominal diameter 121 nm, as imaged by conventional , confocal , and structured illumination microscopy[894]; (c) By using DM and SLM to compensate all of the three path aberrations in STED microscopy[897]; (d) Comparison of Confocal(left) and 3D STED(right) images of Atto647N labelled vesicular glutamate transporter in synaptic boutons in intact Drosophila brains[895]
图 186 透过强散射层非入侵式散射成像示意图[18]
Figure 186. Schematic of the apparatus for non-invasive imaging through strongly scattering layers
图 187 基于单帧散斑自相关的透过强散射层成像[923]。(a)实验装置模型； (b)相机原始图像; (c)自相关; (d)通过迭代相位恢复算法重建物体; (e)实验系统; (f)相机原始数据; (g)~(k)第一列为自相关，第二列为重建物体，第三列为真实的物体
Figure 187. Single frame imaging based on speckle autocorrelation[923] (a) Experimental set-up; (b) Raw camera image; (c) The autocorrelation of the seemingly information-less raw camera image; (d) The object’s image is obtained from the autocorrelation of by an iterative phase-retrieval algorithm; (e) Photograph of the experiment; (f) Raw camera image; (g)-(k) Left column: calculated autocorrelation of the image in (b), Middle column: reconstructed object from the image autocorrelation. Right column: image of the real hidden object
图 189 典型的非视域成像系统示意图
Figure 189. Schematic diagram of typical non field of view imaging system
图 190 （a）捕获过程：通过用脉冲激光依次照亮墙上的单个点并用条纹相机记录墙上虚线段的图像来捕获一系列图像；（b）按顺序收集的条纹图像示例。根据校准信号对强度进行标准化。红色对应于最大强度，蓝色对应于最小强度；（c）通过重建算法恢复的隐藏物体的3D形状的2D投影视图
Figure 190. (a) The capture process: capture a series of images by sequentially illuminating a single spot on the wall with a pulsed laser and recording an image of the dashed line segment on the wall with a streak camera; (b) An example of streak images sequentially collected. Intensities are normalized against a calibration signal. Red corresponds to the maximum, blue to the minimum intensities; (c) The 2D projected view of the 3D shape of the hidden object, as recovered by the reconstruction algorithm
图 191 间接光传输的双重摄影[934]。（a）系统实验装置；（b）室内照明下拍摄到的扑克牌及书本视图；（c）投影仪扫描（d）扑克牌上指示点时获取的样本图像
Figure 191. Dual photography of indirect light transmission[934]. (a) System experimental device; (b) View of playing cards and books taken under indoor lighting; (c) Sample image obtained when the projector scans the indicated points on the playing cards in (d)
图 194 远距离NLOS成像实验。(a) 非视域成像实验的航空示意图；(b)光学仿真结果成像系统的装置图,它由两台同步望远镜组成，分别用于发射和接收；(c)尺寸为2 m×1 m的房间，作为隐藏场景示意图；(d) 非视域成像设置的实际照片；(e)、(f)在A位置拍摄的隐藏场景以及其放大照片，在A位置只能看到可见的墙；(g)隐藏物体的照片，在位于B的房间拍摄
Figure 194. Long-range NLOS imaging experiment. (a) An aerial schematic of the NLOS imaging experiment; (b) The optical setup of the NLOS imaging system, which consists of two synchronized telescopes for transmitter and receiver; (c) Schematic of the hidden scene in a room with a dimension size of 2 m×1 m; (d) An actual photograph of the NLOS imaging setup; (e)-(f) Zoomed-out and zoomed-in photographs of the hidden scene taken at location A, where only the visible wall can be seen; (g) Photograph of the hidden object, taken at the room located at B
图 195 用不同方法比较重建的结果。（a）人体模型隐藏场景的重建结果；（b）字母H隐藏场景的重构结果
Figure 195. Comparison of the reconstructed results with different approaches. (a) The reconstructed results for the hidden scene of mannequin; (b) The reconstructed results for the hidden scene of letter H
图 198 时域高通滤波的非均匀性校正方法
Figure 198. Non-uniformity correction method based om temporal high-pass filter
图 199 长时间运动场景的期望（均值）图像近似满足恒定统计假设
Figure 199. The expected (mean) image of a long-time motion scene approximately satisfies the constant statistical assumption
图 200 各类统计恒定法非均匀性校正的实验对比图。(a) 未校正图像; (b) 多尺度恒定统计; (c) 全局恒定统计; (d) 局部恒定统计
Figure 200. Experimental comparison plots of non-uniformity correction for various types of statistical constancy methods. (a) Uncorrected image; (b) Multiscale constant statistics; (c) Global constant statistics; (d) Local constant statistics
图 201 基于神经网络模型的非均匀性校正方法
Figure 201. Non-uniformity correction method based on neural network
图 203 基于帧间配准的非均匀性校正方法
Figure 203. Nonuniformity correction method based on inter-frame registration
图 204 基于帧间配准的非均匀性校正方法要求精确估计强非均匀性对图像的相对位移
Figure 204. Non-uniformity correction method based on inter-frame registration require accurate estimation of the relative displacement of an image pair imposed by strong non-uniformity
图 205 针对红外探测器的非均匀性和动态范围低等问题，南京理工大学针对性发展高性能红外图像信号处理技术，设计了集成有基于场景非均匀性校正与红外图像数字细节增强等核心算法定制化的ASIC芯片，并基于此研制了高性能无挡片热像仪
Figure 205. In response to the problems of non-uniformity and low dynamic range of infrared detectors, Nanjing University of Science and Technology has developed high-performance infrared image signal processing technology, designed an ASIC with customized core algorithms based on scene-based non-uniformity correction and digital detail enhancement of infrared images, and developed a high-performance shutterless thermal imaging camera
图 206 高端光学设备仪器及其核心器件技术是西方军事强国对我国禁运的“卡脖子”技术和产品
Figure 206. High-end optical instruments and their core technologies are the "bottle-neck" technologies and products embargoed by the Western military powers to China
图 207 中华人民共和国主席令（第一〇三号）中明确指出在功能、质量等指标能够满足需求的条件下，鼓励采购国产科研仪器
Figure 207. The Decree of the President of the People's Republic of China (No. 103) clearly states that under the condition that the function, quality and other indicators can meet the demand, the procurement of domestic scientific research instruments is encouraged
表 1 典型35 mm单反相机镜头的空间带宽积
Table 1. Spatial bandwidth product of typical 35 mm SLR lens
Focal length/mm Field angle
（diagonal）/(°)Typical
F#Equivalent
NAFocal plane resolution
（550 nm）/μmSpatial bandwidth product
Mega pixel/MPMegapixel/mrad 8 180 3.5 0.14 2.396 3.35 0.29 20 94.5 1.8 0.27 1.242 12.4 0.06 50 46.8 1.2 0.41 0.818 28.7 0.016 85 28.6 1.4 0.35 0.959 20.9 0.011 100 24.4 2.8 0.17 1.973 4.9 0.018 200 12.3 4 0.12 2.795 2.4 0.013 400 6.2 5.6 0.08 4.193 1.1 0.009 1000 2.5 8 0.06 5.591 0.61 0.005 
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表 2 典型的显微物镜的空间带宽积
Table 2. Spatial bandwidth product of typical microscopic objectives
Objectives
(Magnification/Numerical aperture/Field number)Resolution/nm
(Incident wavelength 532 nm)SBP/
Megapixel·MP−11.25×/0.04/26.5 8113 21.5 2×/0.08/26.5 4057 33.5 4×/0.16/26.5 2028 33.5 10×/0.3/26.5 1082 18.9 20×/0.5/26.5 649 13.1 40×/0.75/26.5 433 7.4 60×/0.9/26.5 361 4.7 100×/1.3/26.5 250 3.5
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