基于三色LCTF的自然感彩色微光EBAPS成像系统

刘璇; 李瑞强; 李力; 金伟其; 程宏昌; 闫磊; 郑舟

doi:10.5768/JAO202243.0604002

基于三色LCTF的自然感彩色微光EBAPS成像系统

doi: 10.5768/JAO202243.0604002

刘璇^1,,
李瑞强¹,
李力^1, ,,
金伟其¹,
程宏昌²,
闫磊²,
郑舟²

1.
北京理工大学光电学院光电成像技术与系统教育部重点实验室，北京 100081
2.
微光夜视技术重点实验室，陕西西安 710065

基金项目: 国防基础科研项目(2019-JCJQ-ZD-006-00)

详细信息

作者简介:
刘璇（1997—），女，博士研究生，主要从事光电成像理论与技术研究。E-mail：3120205295@bit.edu.cn

通讯作者:
李力（1982—），男，副教授，主要从事光电成像与视频处理技术研究。Email：lili@bit.edu.cn

中图分类号: TN206
计量
- 文章访问数: 65
- HTML全文浏览量: 30
- PDF下载量: 17
- 被引次数: 0
出版历程
- 收稿日期: 2022-08-17
- 修回日期: 2022-10-05
- 刊出日期: 2022-11-14

Natural color low-level-light EBAPS imaging system based on three-color LCTF

1.
Key Laboratory of Photoelectronic Imaging Technology and System (Ministry of Education), School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China
2.
Science and Technology on Low-Light-Level Night Vision Laboratory, Xi'an 710065, China

摘要

摘要: 高灵敏度电子轰击有源像素传感器（electron bombarded active pixel sensor, EBAPS）既有真空成像器件的高增益、高灵敏度特性，又有固体成像器件的数字化特征，极大地提升了夜视成像水平。该文基于三色液晶可调制滤光片(liquid crystal tunable filter, LCTF)搭建了自然感彩色微光EBAPS成像系统，并根据微光图像特性，对系统获得的彩色图像进行了灰度拉伸、白平衡、色彩校正等色彩增强处理。实验结果表明：该系统可实现反映景物本身颜色特点且具有自然感的低照度彩色成像，有效提升夜间对目标景物特点的观察效果，可以在5×10⁻⁴ lx照度下实现彩色成像。
- 电子轰击有源像素传感器 /
- 液晶可调制滤光片 /
- 彩色微光成像 /
- 白平衡 /
- 色彩校正
Abstract: The high-sensitivity electron bombarded active pixel sensor (EBAPS) not only has the characteristics of high gain and high sensitivity of vacuum imaging devices, but also has the digital characteristics of solid-state imaging devices, which greatly improves the level of night vision imaging. A natural color low-level-light EBAPS imaging system was built based on three-color liquid crystal tunable filter (LCTF). According to the characteristics of low-level-light images, the color enhancement processing such as grayscale stretching, white balance, and color correction was performed on the color images obtained by the system. The experimental results show that the system can realize the natural low-illumination color imaging that reflects the color characteristics of the scene itself, which can effectively improve the observation effect of the characteristics of the target scene at night, and can realize the color imaging under 5×10⁻⁴ lx illumination.
- electron bombarded active pixel sensor /
- liquid crystal tunable filter /
- color low-level-light imaging /
- white balance /
- color correction

HTML全文

图 1 EBAPS成像原理示意图

Fig. 1 Schematic diagram of EBAPS imaging principle

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图 2 彩色微光EBAPS图像采集实验系统

Fig. 2 Experimental system of color low-level-light EBAPS image acquisition

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图 3 系统模型

Fig. 3 Diagram of system model

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图 4 Meadowlark Optics的三色LCTF及其控制器

Fig. 4 Three-color LCTF and its controller of Meadowlark Optics

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图 5 EBAPS及其系统实物

Fig. 5 EBAPS and physical integrated system

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图 6 几种典型光电阴极的光谱响应

Fig. 6 Spectral responses of several typical photocathodes

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图 7 EBAPS成像过曝区域及噪声点

Fig. 7 Imaging overexposed areas and noise spots of EBAPS

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图 8 实验所用标准色卡

Fig. 8 Standard color checkers used in experiment

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图 9 色块光谱反射率

Fig. 9 Spectral reflectance of color blocks

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图 10 2种颜色空间模型与2种回归方法计算彩色校正结果

Fig. 10 Color correction results calculated by two color space models and two regression methods

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图 11 场景1在不同照度下图像处理效果

Fig. 11 Image processing effect under different illuminance in scene 1

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图 12 场景2在不同照度下图像处理效果

Fig. 12 Image processing effect under different illuminance in scene 2

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表 1 回归方法及其计算形式

Table 1 Regression method and its calculation form

回归方法	色彩校正矩阵T_CCM计算形式
QLSR	${{{\boldsymbol{T}}}_{{\text{CCM}}}}{ = }{{\boldsymbol{Q}}}{{{\boldsymbol{P}}}^{\rm{T}}}{({{\boldsymbol{P}}}{{{\boldsymbol{P}}}^{\rm{T}}})^{ - 1}}$
PLSR	${{{\boldsymbol{T}}}_{{\text{CCM}}}}{ = }{{\boldsymbol{Q}}}{{{\boldsymbol{P}}}^{\rm{T}}}{({{\boldsymbol{P}}}{{{\boldsymbol{P}}}^{\rm{T}}} + \lambda {{\boldsymbol{I}}})^{ - 1}}$

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表 2 颜色空间模型

Table 2 Color space model

颜色空间模型	标准矩阵Q	测量矩阵P	变换形式
线性模型	${{\boldsymbol{Q}}} = \left[ {\begin{array}{*{20}{c}} {{R_{\text{S}}}}&{{G_{\text{S}}}}&{{B_{\text{S}}}} \end{array}} \right]$	${{\boldsymbol{P}}} = \left[ {\begin{array}{*{20}{c}} {{R_{\text{M}}}}&{{G_{\text{M}}}}&{{B_{\text{M}}}} \end{array}} \right]$	${{{\boldsymbol{Q}}}^{\rm{T}}} = {{{\boldsymbol{T}}}_{{\text{CCM}}}}{{{\boldsymbol{P}}}^{\rm{T}}}$
复合模型	${{\boldsymbol{Q}}} = \left[ {\begin{array}{*{20}{c}} {\ln ({R_{\text{S}}})}&{\ln ({G_{\text{S}}})}&{\ln ({B_{\text{S}}})} \end{array}} \right]$	$ {{\boldsymbol{P}}} = \left[ {\begin{array}{*{20}{c}} {{R_{\text{M}}}}&{{G_{\text{M}}}}&{{B_{\text{M}}}}&1 \end{array}} \right] $	${{{\boldsymbol{Q}}}^{\rm{T}}} = {{{\boldsymbol{T}}}_{{\text{CCM}}}}{{{\boldsymbol{P}}}^{\rm{T}}}$

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表 3 场景1图像评价指标参数

Table 3 Image evaluation index parameters of scene 1

图像	色差/NBS			饱和度
图像	拉伸后图像	白平衡后图像	色彩校正图像	白平衡后图像	色彩校正图像
(a)	31.7691	29.7584	16.8068	0.1844	1.1775
(b)	31.1937	30.4833	18.9607	0.1635	1.0684
(c)	36.5345	29.0815	18.9607	0.2215	1.0684
(d)	30.4207	36.7243	20.2295	0.1331	1.1416
(e)	31.8976	41.8603	22.7804	0.1000	1.1341
(f)	34.1854	44.2036	25.6088	0.0947	1.1602
(g)	39.8942	48.1827	29.4266	0.0718	1.0503
(h)	49.5305	53.8235	27.1670	0.0285	0.8663

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