中波红外相机盲元的实时动态检测与补偿方法

孙超; 张洪文; 王沛; 李军

中波红外相机盲元的实时动态检测与补偿方法

孙超^{1, 2,},
张洪文^{1, 2},
王沛³,
李军^{1, 2}

1.
中国科学院航空光学成像与测量重点实验室, 吉林长春 130033
2.
中国科学院长春光学精密机械与物理研究所, 吉林长春 130033
3.
北京长峰科威光电技术有限公司, 北京 100195

详细信息

作者简介:
孙超(1986-), 男, 吉林四平人, 助理研究员, 研究方向为航空光学成像与测量技术。E-mail: chaosxjtu@163.com

中图分类号: TN215
计量
- 文章访问数: 387
- HTML全文浏览量: 58
- PDF下载量: 103
出版历程
- 收稿日期: 2021-05-11
- 修回日期: 2021-08-06
- 刊出日期: 2021-09-19

Real-time Dynamic Blind Pixel Detection and Compensation Method for Mid-wave Infrared Camera

SUN Chao^{1, 2,},
ZHANG Hongwen^{1, 2},
WANG Pei³,
LI Jun^{1, 2}

1.
Key Laboratory of Airborne Optical Imaging and Measurement, Chinese Academy of Sciences, Changchun 130033, China
2.
Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China
3.
Beijing Chang Feng Ke Wei Photoelectric Technology Co. Ltd., Beijing 100195, China

摘要

摘要: 盲元的存在严重影响了红外相机的成像质量，基于场景的盲元检测与补偿方法可以有效地解决此类问题。本文提出了一种改进的局部“3σ”方法，通过计算图像的三维噪声获得图像的平均噪声，据此得到盲元检测的最小判据，然后采用局部“3σ”方法和中值滤波法对盲元进行实时的动态检测与补偿，并将该方法应用于自研的某中波红外相机中。对黑体成像实验的结果表明，本文方法与辐射定标法相比，盲元检出的重合度平均可以达到82%以上；与传统的局部“3σ”方法相比具有相同的盲元检测与补偿效果，但可以将盲元的过检率降低30%以上；地面及载机挂飞成像实验的结果表明，本文方法可以对盲元起到很好地抑制作用，红外相机的昼间和夜间图像均不存在明显异常的黑、白点，图像中景物细节丰富、图像质量优良。因此，本文方法可以对盲元进行实时的动态检测与补偿，在自研的中波红外相机中的运用是可行和有效的。
- 红外相机 /
- 盲元检测 /
- 盲元补偿 /
- 3σ /
- 三维噪声
Abstract: The existence of blind pixels significantly affects the imaging quality of infrared cameras. Scene-based methods for blind pixel detection and compensation can effectively solve such problems. This study proposes an improved local "3σ" method. The average noise of the infrared image is obtained by calculating the three-dimensional noise of the image; according to this average noise, the minimum criterion for blind pixel detection is obtained. Subsequently, real-time dynamic detection and compensation for blind pixel is performed by local "3σ" and median filtering method; furthermore, the method was applied to a self-developed mid-wave infrared camera. The results of the blackbody imaging experiment show that, compared with the radiation calibration method, the coincidence degree of blind pixel detection of our method can exceed 82% on average. The proposed method has the same effect of detection and compensation for blind pixel compared with the traditional local "3σ" method; however, it can reduce the over-detection rate of blind pixel by more than 30%.The results of imaging experiments of ground scene show that the proposed method can effectively restrain the blind pixel, and no apparent abnormal black and white spots were found in both daytime and nighttime images captured by the infrared camera. Additionally, the scene details in the images are rich, and the image quality is excellent. Therefore, the method presented in this study has good performance in real-time dynamic blind pixel detection and compensation. It is feasible and effective for use in self-developed mid-wave infrared cameras.
- infrared camera /
- blind pixel detection /
- blind pixel compensation /
- 3σ /
- three-dimensional noise

HTML全文

图 1 红外图像对比（局部）

Figure 1. Comparison of infrared images(partial)

下载: 全尺寸图片幻灯片

图 2 含有盲元的红外相机航拍图像（局部）

Figure 2. Aerial image with blind pixel of infrared camera (partial)

下载: 全尺寸图片幻灯片

图 3 红外图像的灰度空间分布

Figure 3. Spatial distribution of grayscale in an IR image

下载: 全尺寸图片幻灯片

图 4 红外图像对比（局部）

Figure 4. Comparison of infrared images (partial)

下载: 全尺寸图片幻灯片

图 5 开启盲元补偿功能的地面外景红外图像

Figure 5. Infrared image of ground after turning on the blind pixel compensation function

下载: 全尺寸图片幻灯片

图 6 昼间红外相机航拍图像

Figure 6. Aerial image taken by infrared camera in the daytime

下载: 全尺寸图片幻灯片

图 7 夜间红外相机航拍图像

Figure 7. Aerial image taken by infrared camera at night

下载: 全尺寸图片幻灯片

表 1 辐射定标法和本文方法的对比（20℃黑体成像）

Table 1 Comparison of methods of radiation calibration and this paper (imaging blackbody at 20℃)

Image’s number	Method of radiation calibration	Method of this paper	Coincidence number of blind pixel detection	Coincidence percentage of blind pixel detection
1	4791	8679	4096	85.49%
2	5856	9742	4880	83.33%
3	6220	9809	5015	80.63%
4	6089	10331	5041	82.79%
5	4940	9502	4208	85.18%
6	5074	9466	4264	84.04%
7	6328	9989	5104	80.66%
8	5768	9569	4726	81.93%
9	6690	10736	5379	80.40%
10	5058	9008	4272	84.46%
On average	5681	9683	4699	82.71%

下载: 导出CSV

表 2 局部“3σ”方法和本文方法的对比（20℃黑体成像）

Table 2 Comparison of methods of local "3σ" and this paper (imaging blackbody at 20℃)

Algorithm	Method of local “3σ”	Method of this paper
Average noise after blind pixel compensation	5.4016	5.4263
Number of blind pixel in original image	4378	4311
Number of residual blind pixel after compensation	135	135
Average number of blind pixel compensated for 20 images	14205	10091

下载: 导出CSV

表 3 局部“3σ”方法和本文方法的对比（5℃黑体成像）

Table 3 Comparison of methods of local "3σ" and this paper(imaging blackbody at 5℃)

Algorithm	Method of local “3σ”	Method of this paper
Average noise after blind pixel compensation	5.0913	5.1050
Number of blind pixel in original image	2179	2155
Number of residual blind pixel after compensation	36	34
Average number of blind pixel compensated for 20 images	13142	9814

下载: 导出CSV

参考文献(16)

[1]	沈宏海, 黄猛, 李嘉全, 等. 国外先进航空光电载荷的进展与关键技术分析[J]. 中国光学, 2012, 5(1): 20-29. DOI: 10.3969/j.issn.2095-1531.2012.01.003 SHEN Honghai, HUANG Meng, LI Jiaquan, et al. Recent progress in aerial electro - optic payloads and their key technologies[J]. Chinese Optics, 2012, 5(1): 20-29. DOI: 10.3969/j.issn.2095-1531.2012.01.003
[2]	王岳, 李双喜, 王磊. 红外航空相机技术研究[J]. 激光与红外, 2017, 47(12): 1468-1472. DOI: 10.3969/j.issn.1001-5078.2017.12.003 WANG Yue, LI Shuangxi, WANG Lei. Study on infrared aerial camera technology[J]. Laser & Infrared, 2017, 47(12): 1468-1472. DOI: 10.3969/j.issn.1001-5078.2017.12.003
[3]	李凌霄, 冯华君, 赵巨峰, 等. 红外焦平面阵列的盲元自适应快速校正[J]. 光学精密工程, 2017, 25(4): 477-486. https://www.cnki.com.cn/Article/CJFDTOTAL-GXJM201704024.htm LI Lingxiao, FENG Huajun, ZHAO Jufeng, et al. Adaptive and fast blind pixel correction of IRFPA[J]. Optics and Precision Engineering, 2017, 25(4): 477-486. https://www.cnki.com.cn/Article/CJFDTOTAL-GXJM201704024.htm
[4]	GHOSH S, MARSHALL I, FREITAS A. Autonomously detecting the defective pixels in an imaging sensor array using a robust statistical technique[C]//Proc. SPIE, Image Quality and System Performance V, San Jose, CA, 2008, 6808: 680813-1-680813-12.
[5]	刘高睿, 孙胜利, 林长青, 等. 红外线列探测器闪元噪声分析与抑制方法[J]. 红外与毫米波学报, 2018, 37(4): 421-426. https://www.cnki.com.cn/Article/CJFDTOTAL-HWYH201804008.htm LIU Gaorui, SUN Shengli, LIN Changqing, et al. Analysis and suppression method of flickering pixel noise in images of infrared linear detector[J]. J. Infrared Millim. Waves, 2018, 37(4): 421-426. https://www.cnki.com.cn/Article/CJFDTOTAL-HWYH201804008.htm
[6]	张长兴, 刘成玉, 丌洪兴, 等. 热红外高光谱成像仪光谱匹配盲元检测算法[J]. 红外与激光工程, 2020, 49(1): 0104002-1-0104002-7. https://www.cnki.com.cn/Article/CJFDTOTAL-HWYJ202001014.htm ZHANG Changxing, LIU Chengyu, QI Hongxing, et al. Blind pixel detection algorithm using spectral matching for thermal infared hyperspectral imager[J]. Infrared and Laser Engineering, 2020, 49(1): 0104002-1-0104002-7. https://www.cnki.com.cn/Article/CJFDTOTAL-HWYJ202001014.htm
[7]	周慧鑫, 魏亚姣, 秦翰林, 等. 采用双阈值的非制冷IRFPA盲元迭代检测算法[J]. 红外与激光工程, 2011, 40(5): 795-799. DOI: 10.3969/j.issn.1007-2276.2011.05.004 ZHOU Huixin, WEI Yajiao, QIN Hanlin, et al. Blind-pixel iterative detection algorithm based on double threshold for uncooled IRFPA[J]. Infrared and Laser Engineering, 2011, 40(5): 795-799. DOI: 10.3969/j.issn.1007-2276.2011.05.004
[8]	冷寒冰, 宫振东, 谢庆胜, 等. 基于模糊中值的IRFPA自适应盲元检测与补偿[J]. 红外与激光工程, 2015, 44(3): 821-826. DOI: 10.3969/j.issn.1007-2276.2015.03.006 LENG Hanbing, GONG Zhendong, XIE Qingsheng, et al. Adaptive blind pixel detection and compensation for IRFPA based on fuzzy median filter[J]. Infrared and Laser Engineering, 2015, 44(3): 821-826. DOI: 10.3969/j.issn.1007-2276.2015.03.006
[9]	顾国华. 基于滑动窗口与多帧补偿的自适应盲元检测与补偿算法[J]. 红外技术, 2010, 32(7): 420-423. DOI: 10.3969/j.issn.1001-8891.2010.07.013 GU Guohua. A Blind Pixel Self-adaptive Detection And Compensation Algorithm Based on Sliding Window and Multi-frame Compensation[J]. Infrared Technology, 2010, 32(7): 420-423. DOI: 10.3969/j.issn.1001-8891.2010.07.013
[10]	郑骁, 葛志雄, 赖永安. 基于滑动窗口的红外焦平面阵列盲元检测算法研究[J]. 红外技术, 2019, 41(8): 735-741. http://hwjs.nvir.cn/article/id/hwjs201908008 ZHENG Xiao, GE Zhixiong, LAI Yong'an. Algorithm for Blind-pixel Detection of IRFPA Based on Sliding Window[J]. Infrared Technology, 2019, 41(8): 735-741. http://hwjs.nvir.cn/article/id/hwjs201908008
[11]	詹维, 马新星, 徐子剑. 基于超像素分割的红外盲元检测及校正[J]. 红外技术, 2018, 40(11): 1085-1090. http://hwjs.nvir.cn/article/id/hwjs201811012 ZHAN Wei, MA Xinxing, XU Zijian. IR Blind Pixels Detection and Correction Based on Superpixel Segmentation[J]. Infrared Technology, 2018, 40(11): 1085-1090. http://hwjs.nvir.cn/article/id/hwjs201811012
[12]	张北伟, 曹江涛, 丛秋梅. 基于曲面拟合的红外图像盲元检测方法[J]. 红外技术, 2017, 39(11): 1007-1011. http://hwjs.nvir.cn/article/id/hwjs201711007 ZHANG Beiwei, CAO Jiangtao, CONG Qiumei. Detection Method for Infrared-image Blind Pixels Based on Curved-surface Fitting[J]. Infrared Technology, 2017, 39(11): 1007-1011. http://hwjs.nvir.cn/article/id/hwjs201711007
[13]	粟宇路, 苏兰, 陈大乾, 等. 基于分布搜索策略的自适应盲元检测算法[J]. 红外技术, 2016, 38(6): 457-460. http://hwjs.nvir.cn/article/id/hwjs201606002 SU Yulu, SU Lan, CHEN Daqian, et al. Adaptive Blind Pixel Detection Algorithms Based on Stepwise Search Strategy[J]. Infrared Technology, 2016, 38(6): 457-460. http://hwjs.nvir.cn/article/id/hwjs201606002
[14]	国家技术监督局. GB/T 17444-2013. 红外焦平面阵列参数测试方法[S]. 北京: 中国标准出版社, 2014. The State Bureau of Quality and Technical Supervision. GB/T 17444-2013. Measuring methods for parameters of infrared focal plane arrays[S]. Beijing: Standards Press of China, 2014.
[15]	John D Agostino, Curtis Webb. Three-dimensional analysis framework and measurement methodology for imaging system noise[C]//Proc. SPIE, Infrared Imaging Systems: Design, Analysis, Modeling, and Testing Ⅱ, 1991, 1488: 110-121.
[16]	Patrick O'Shea, Stephen Sousk. Practical Issues with 3D-Noise Measurements and Application to Modern Infrared Sensors[C]//Proc. SPIE, Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XVI, 2005, 5784: 262-271.