Research advances in simple and compact optical imaging techniques

Liu You-Hai; Qin Tian-Xiang; Wang Ying-Ce; Kang Xing-Wang; Liu Jun; Wu Jia-Chen; Cao Liang-Cai

doi:10.7498/aps.72.20230092

Abstract

Computational imaging enables optical imaging systems to acquire more information with miniaturized setups. Computational imaging can avoid the object-image conjugate limitation of the imaging system, and introduce encoding and decoding processes based on physical optics to achieve more efficient information transmission. It can simultaneously increase the amount of information and reduce the complexity of the system, thereby paving the way for miniaturizing imaging systems. Based on computational imaging, the simple and compact optical imaging techniques are developed, which is also called simple optics. To develop miniaturized optical imaging elements and integrated systems, simple optics utilizes the joint design of optical system and image processing algorithms, thereby realizing high-quality imaging that is comparable to complex optical systems. The imaging systems are of small-size, low-weight, and low-power consumption. With the development of micro-nano manufacturing, the optical elements have evolved from a single lens or a few lenses, to flat/planar optical elements, such as diffractive optical elements and metasurface optical elements. As a result, various lensless and metalens imaging systems have emerged. Owing to the introduction of encoding process and decoding process, an optical imaging model is developed to represent the relationship between the target object and the acquired signal, from which the computational reconstruction is used to restore the image. In the image restoration part, the algorithms are discussed in three categories, i.e. the classic algorithm, the model-based optimization iterative algorithm, and the deep learning (neural network) algorithm. Besides, the end-to-end optimization is highlighted because it introduces a new frame to minimize the complexity of optical system. In this review, the imaging techniques realized by simple optics are also discussed, such as depth imaging, high-resolution and super-resolution imaging, large field of view imaging, and extended depth of field imaging, as well as their important roles in developing consumer electronics, unmanned driving, machine vision, security monitoring, biomedical devices and metaverse. Last but not least, the challenges and future developments are prospected.

Keywords:

Authors and contacts

Department of Precision Instrument, Tsinghua University, Beijing 100084, China

Corresponding author: Cao Liang-Cai, clc@tsinghua.edu.cn

Funds: Project supported by the National Natural Science Foundation of China (Grant No. 62235009).

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Cited By

图 1 简单光学成像系统示意图

Figure 1. Schematic of the simple optical imaging system.

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图 2 单透镜成像系统^[33]

Figure 2. The single lens imaging system^[33].

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图 3 折衍射混合式成像系统^[35]

Figure 3. The diffractive refractive hybrid imaging system^[35].

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图 4 简单光学系统设计结构图和复原结果比较^[40]

Figure 4. Layout schematic of simple optical system and the comparison of restoration results^[40].

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图 5 光学系统结构的最终设计结果^[47]

Figure 5. Final design results of the optical system configuration^[47].

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图 6 无透镜成像分类

Figure 6. The category of lensless imaging.

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图 7 照明调制　(a)片上光流体显微成像示意图和装置顶视图^[57]; (b)基于多角度照明的合成孔径无透镜显微成像装置^[60]

Figure 7. Illumination modulated lensless systems: (a) Schematic diagram of the on-chip optofluidic microscopy and top view of the device^[57]; (b) synthetic aperture lensless microscopic imaging device based on multi angle illumination^[60].

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图 8 掩膜调制成像相机的发展历程 2013年Rambus实验室提出相位非对称旋转光栅^[77]; 2015年BAE Systems 公司提出可分离Doubly-Toeplitz掩膜^[69], Rice大学提出FlatCam^[70]; 2017年UC Berkeley提出DiffuserCam^[78], 日立公司提出基于菲涅耳孔径的振幅掩膜^[73]; 2020年清华大学提出菲涅耳孔径编码^[74], UC Berkeley提出用于光谱成像的散射介质相机^[79], Rice大学提出基于相位掩膜的PhlatCam^[82]; 2021年KAIST提出用于光谱深度成像的DOE^[85]; 2022年清华大学提出无透镜复眼微系统^[75]

Figure 8. Development of mask-modulated lensless camera. Rambus lab proposed the phase anti-symmetric spiral gratings^[77] in 2013; BAE Systems proposed separable Doubly-Toeplitz masks^[69], and Rice University proposed FlatCam^[70] in 2015; UC Berkeley proposed DiffuserCam^[78] and Hitachi proposed amplitude mask based on Fresnel zone aperture^[73] in 2017; Tsinghua University proposed the Fresnel zone aperture^[74], UC Berkeley proposed Spectral DiffuserCam^[79], and Rice University proposed phase mask based PhlatCam^[82] in 2020; KAIST proposed DOE^[85] for Hyperspectral-Depth Imaging in 2021; Tsinghua University proposed lensless compound microsystem^[75] in 2022.

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图 9 (a) 基于共振相位的超构表面; (b) 基于传播相位的介质脊波导; (c) 庞加莱球与基于几何相位的超构表面

Figure 9. (a) Plasmonic metasurface based on resonance phase; (b) dielectric ridge waveguide based on propagation phase; (c) schematic of the geometric phase with Poincare sphere.

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图 10 (a) 惠更斯超构表面以及电偶极子和磁偶极子的电磁场分布^[91]; (b) 硅超构表面示意图以及不同周期超构表面的透射强度随硅柱直径和波长的变化^[92]; (c) 介质脊波导结构图以及xz面电场分布^[93]; (d) 超构透镜结构单元的侧视图和俯视图以及超构表面的模拟相位图^[94]; (e) 超构表面单元与几何相位示意图^[96]; (f) 基于耦合纳米棒的集成谐振单元的相位分布^[97]

Figure 10. (a) The Huygens’ metasurface as well as the electromagnetic field distribution of electric and magnetic dipoles^[91]; (b) schematic of silicon metasurface and the transmission intensity of metasurface depends on the diameter and wavelength of silicon cylinder^[92]; (c) schematic diagram of dielectric ridge waveguide and electric field distribution in xz-plane^[93]; (d) side-view and top-view of the metasurface building block and simulated phase map for the metasurface^[94]; (e) schematic for the metasurface building block and the geometric phase^[96]; (f) phase profile of integrated-resonant unit elements based on coupled nano-rods^[97].

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图 11 超构透镜相机　(a) 薄饼超构透镜相机^[100]; (b) 盐粒大小的超构透镜^[110]; (c) 超构透镜热成像相机^[102]; (d) 近红外超构透镜相机^[104]; (e) 长波红外超构透镜相机^[107]; (f) 超构透镜阵列集成广角相机^[108]

Figure 11. Metalens cameras: (a) Pancake metalens camera^[100]; (b) metalens of salt grain size^[110]; (c) metalens thermal imaging camera^[102]; (d) near infrared metalens camera^[104]; (e) long-wave infrared metalens camera^[107]; (f) metalens array integrated wide-angle camera^[108]

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图 12 压缩感知的数学模型^[53]

Figure 12. Mathematical models of compressive sensing^[53].

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图 13 编码成像系统分类　(a) 一对一映射编码已知成像系统; (b) 一对多映射编码已知成像系统; (c) 一对多映射编码未知成像系统

Figure 13. Classification of coding imaging system: (a) One-to-one map encoding with known imaging systems; (b) one-to-many map encoding with known imaging systems; (c) one-to-many map encoding with unknown imaging systems.

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图 14 基于深度学习的光谱图像重建^[119]

Figure 14. Spectral image reconstruction based on deep learning^[119]

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图 15 端到端处理流程

Figure 15. End-to-end pipeline.

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图 16 简单光学成像技术分类. 按照顺时针排列依次为: 深度成像, 例如PhaseCam3D^[36], 超构透镜阵列深度传感系统^[160]; 高分辨与超分辨成像, 例如端到端优化得到的DOE实现超分辨率成像^[156], FlatScope原型样机拍照并计算重建图像^[71]; 大景深成像, 例如多级衍射透镜的极深焦距成像^[161], 由自旋复用超构透镜阵列实现的光场成像系统^[162]; 大视场成像, 例如片上集成μ-CE相机^[163], 片上180°×360°圆顶成像系统^[164], 超构透镜阵列集成广角相机^[108]

Figure 16. Categories of simple optical imaging techniques. In clockwise order, they are: Depth imaging, e.g., PhaseCam3D^[36], the achromatic meta-lens array depth-sensing system^[160]; high resolution and super-resolution imaging, e.g., DOE designed by end-to-end optimization for super-resolution^[156], imaging by the FlatScope prototype and computational reconstruction of the image^[71]; large depth of field imaging, e.g., the multi-level diffractive lens that exhibits extreme-depth-of-focus imaging^[161], light-field imaging system enabled by the spin-multiplexed metalens array^[162]; wide field-of-view imaging, e.g., the on-chip integrated μ-CE camera^[163], the on-chip 180°×360° imaging system^[164], metalens array integrated wide-angle camera^[108].

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表 1 简单透镜成像系统及其实现方法

Table 1. Simple lens imaging system and its implementation.

序号	成像元件	方法	作者
1	单透镜	估计单透镜PSF函数, 后利用非盲去卷积算法复原图像	Schuler等^[30]
2	单透镜	估计单透镜PSF函数, 再基于交叉通道先验执行非盲去卷积算法复原图像	Heide等^[31]
3	单透镜	使用快速可微光线追迹模型和基于Res-Unet的恢复网络实现单镜头端到端设计和高质量成像	Li等^[34]
4	折衍混合透镜	由可微分光学层的深度相关PSF对全聚焦图像进行编码, 再利用基于U-Net的深度网络对编码图像进行深度图重构	Wu等^[36]
5	折衍混合透镜	设计一款菲涅耳透镜, 通过混合PSF在整个视场上产生空间位移不变的点扩散函数, 建立基于变体U-Net、生成对抗网络和知觉损失的深度学习网络实现高质量图像重建	Peng等^[38]
6	透镜组	利用基于噪声图像对的正态Sinh-Arcsinh模型的单镜头相机PSF估计方法, 通过非盲去卷积算法获得高质量图像	Zhan等^[41]
7	透镜组	利用一个基于可微分光线追迹渲染引擎的端到端复杂透镜的设计框架对特定成像任务联合优化镜头模块和图像重建网络, 重建网络采用U-Net架构	Sun等^[42]
8	透镜组	对简单光学模块引入加权斑块退化模型, 建立DMPH-SE网络实现高质量图像重建	Ji等^[44]

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表 2 衍射光学元件成像系统

Table 2. Diffractive optical elements imaging system.

序号	工作波长/nm	特点	作者
1	410—690	使用优化方法重新排列PSF的空间和光谱分布, 在硬件上减小色差, 使用交叉尺度先验去卷积重建图像	Peng等^[80]
2	400—700	将DOE结构高度编码并使用粒子群算法进行优化, 各波长模糊核趋于一致, 从而降低去卷积复原的难度	Zhao等^[81]
3	Visible (designed at 532)	设计具有轮廓线型PSF的相位掩模, 并使用全变差正则化先验去卷积复原图像, 实现三维成像	Boominathan等^[82]
4	420—720	联合优化DOE的高度和图像处理模块的参数, 使用维纳滤波复原图像	Dun等^[83]
5	429—699	使用同心环分解的旋转对称衍射消色差设计, 并使用Res-Unet复原图像, 具有高保真成像性能	Dun等^[84]
6	420—680	搭建可微分模拟器和神经网络重建方法进行联合优化, 能够实现高光谱深度成像	Baek等^[85]
7	Visible (designed at 550)	使用多个DOE堆叠实现变焦, 使用交叉通道先验去卷积复原图像	Heide等^[86]
8	875—1675	使用多级衍射透镜实现短波红外成像, 使用维纳滤波去卷积复原图像	Banerji等^[87]

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表 3 超构透镜相机

Table 3. Metalens cameras.

序号	工作波长/μm	特点	作者
1	0.98	采用单片直径为320 μm的a-Si/SiO₂超构透镜, 焦距为800 μm, NA为0.42	Chen等^[100]
2	0.4—0.7	采用单片直径为0.5 mm的Si₃N₄/SiO₂超构透镜, 焦距为1 mm, FOV为40°	Tseng等^[101]
3	9.3—10.6	采用单片直径为12 mm的Si/蓝宝石超构透镜, 焦距为4.48 mm, FOV为178°	Zhang等^[102]
4	9—12	采用2×3个直径为1.7234 mm的全Si超构透镜, 焦距为1 cm, 将平均串扰降低3倍以上	Zhang等^[103]
5	1.55	采用单片直径为2 cm的a-Si/SiO₂超构透镜, 焦距为50 mm, NA为0.2	She等^[104]
6	0.42—0.65	采用双筒直径为2.6 mm的GaN超构透镜, 焦距为10 mm, 深度测量精度可达50 μm	Liu等^[105]
7	0.8	采用多个直径为2 mm的a-Si/SiO₂超构透镜, 焦距为30 mm, 利用合成孔径实现了与大孔径常规透镜相当的成像分辨率	Zhao等^[106]
8	10.6	采用2×2个直径为5 cm的全Si超构透镜阵列, 焦距34 mm, NA为0.592	Hou等^[107]
9	0.47	采用1×17个直径为0.3 mm的Si₃N₄/SiO₂超构透镜阵列, 焦距为450 μm, FOV>120°	Chen等^[108]
10	0.63	采用6×6个直径为0.2 mm的a-Si/Si超构透镜阵列, 焦距为250 μm, NA为0.37	Xu等^[109]

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