Adaptive bilocal enhancement algorithm for infrared image with extreme temperature difference

He Ming; Wang Yadi; Wang Xinsai; Lu Jianfang; Zhang Yi; Li Zhijun

doi:10.11884/HPLPB201426.081006

Volume 26 Issue 08

Jul. 2014

Turn off MathJax

Article Contents

Abstract

References

Article Navigation > High Power Laser and Particle Beams > 2014 > 26(08): 081006

He Ming, Wang Yadi, Wang Xinsai, et al. Adaptive bilocal enhancement algorithm for infrared image with extreme temperature difference[J]. High Power Laser and Particle Beams, 2014, 26: 081006. doi: 10.11884/HPLPB201426.081006

Citation:

He Ming, Wang Yadi, Wang Xinsai, et al. Adaptive bilocal enhancement algorithm for infrared image with extreme temperature difference[J]. High Power Laser and Particle Beams, 2014, 26: 081006. doi: 10.11884/HPLPB201426.081006

Citation:

PDF( 2315 KB)

Adaptive bilocal enhancement algorithm for infrared image with extreme temperature difference

doi: 10.11884/HPLPB201426.081006

He Ming^1,2,3,
Wang Yadi^{1
,
,},
Wang Xinsai^1,2,3,
Lu Jianfang^1,2,3,
Zhang Yi²,
Li Zhijun²

1.
PLA Information Engineering University,Zhengzhou 450002,China;
2.
Air Defense Forces Academy,Zhengzhou 450002,China;
3.
Shanghai Science and Technology Development Cp Lt,Shanghai 201821,China

Received Date: 2013-12-02
Rev Recd Date: 2014-04-09
Publish Date: 2014-06-26

Abstract

Abstract

The infrared focal plane system cant provide well distributed gray of every temperature section and sufficient image details for targets with extreme temperature difference. To address this problem, an adaptive bilocal enhancement algorithm was proposed. This paper gives a detailed introduction about the adaptive bilocal enhancement algorithm from two aspects: spatial distribution and gray statistics characteristics. This algorithm adaptively divides the original image into several sub-images based on the spatial distribution of infrared image, then gets the local histogram statistics based on the local gray statistics characteristics, and do adaptive segment equalization on the histogram of every local space. At last, the enhanced images were mosaicked by linear interpolation algorithm. The practicability of this algorithm has been verified by a series of experiments on the infrared focal plane system. The effect was satisfactory and the imaging quality was improved significantly.
- infrared image,
- extreme temperature,
- adaptive bilocal enhancement,
- histogram equalization

FullText(HTML)

References(0)

References

Relative Articles

[1]	Qian Kun, Yang Junyan, Yu Yue, Zhao Dong, Rong Shenghui. Infrared target tracking based on selective convolution features[J]. High Power Laser and Particle Beams, 2019, 31(9): 093202. doi: 10.11884/HPLPB201931.190133
[2]	Cai Qing, Liu Huiying, Zhou Sanping, Sun Jingfeng. Adaptive level set model based on local and global intensity information for image segmentation[J]. High Power Laser and Particle Beams, 2017, 29(02): 021003. doi: 10.11884/HPLPB201729.160432
[3]	Qin Hanlin, Zeng Qingjie, Li Jia, Zhou Huixin, Yan Xiang, Han Jiaojiao, Lv Enlong. Low-contrast infrared image real-time enhancement based on singular value nonlinear correction[J]. High Power Laser and Particle Beams, 2015, 27(01): 011007. doi: 10.11884/HPLPB201527.011007
[4]	You Anqing, Wang Lei, Zhang Rong, Song Haifeng. Detecting approximately-circular object in image[J]. High Power Laser and Particle Beams, 2015, 27(12): 121004. doi: 10.11884/HPLPB201527.121004
[5]	Ling Qiang, Huang Shucai, Wu Xiao, Zhong Yu. Infrared small target detection based on kernel anisotropic diffusion[J]. High Power Laser and Particle Beams, 2015, 27(01): 011014. doi: 10.11884/HPLPB201527.011014
[6]	Qu Shiru, Yang Honghong. Infrared image segmentation based on PCNN with genetic algorithm parameter optimization[J]. High Power Laser and Particle Beams, 2015, 27(05): 051007. doi: 10.11884/HPLPB201527.051007
[7]	Wang Xiaoyang, Peng Zhenming, Zhang Ping, Meng Yeming. Infrared small dim target detection based on local contrast combined with region saliency[J]. High Power Laser and Particle Beams, 2015, 27(09): 091005. doi: 10.11884/HPLPB201527.091005
[8]	Wen Nu, Yang Shizhi, Cui Shengcheng, Cheng Wei. Adaptive dual-tree complex wavelet algorithm for remote sensing image restoration[J]. High Power Laser and Particle Beams, 2014, 26(10): 101003. doi: 10.11884/HPLPB201426.101003
[9]	Ma Ke, Peng Zhenming, He Yanmin, Gao Yuan, Zhang Ping. An improved method for dim infrared target detection with nonsubsampled Contourlet transform[J]. High Power Laser and Particle Beams, 2013, 25(11): 2811-2815. doi: 10.3788/HPLPB20132511.2811
[10]	Bai Honggang, Zhang Jianqi, Wang Xiaorui. Infrared complex background clutter suppression algorithm based on wave atom transform[J]. High Power Laser and Particle Beams, 2013, 25(01): 37-41. doi: 10.3788/HPLPB20132501.0037
[11]	Jing Liang, Peng Zhenming, He Yanmin, Pu Tian. Infrared dim target detection based on anisotropic SUSAN filtering[J]. High Power Laser and Particle Beams, 2013, 25(09): 2208-2212. doi: 10.3788/HPLPB20132509.2208
[12]	Qin Hanlin, Huang Yang, Yao Keke, Zhou Huixin, Liu ShangQian. Multi-scale kernel local normalization for infrared image background suppression[J]. High Power Laser and Particle Beams, 2012, 24(05): 1063-1066. doi: 10.3788/HPLPB20122405.1063
[13]	huang yonglin, ye yutang, qiao naosheng, chen zhenlong. Infrared image segmentation based on fast fuzzy C-means clustering[J]. High Power Laser and Particle Beams, 2011, 23(06): 0- .
[14]	zhang libao, li dongling, yu xianchuan, wang pengfei, cai lei. Region-of-interest image edge detection based on histogram[J]. High Power Laser and Particle Beams, 2010, 22(08): 0- .
[15]	li fan, liu shangqian, hong ming, qin hanlin. Algorithm of infrared image segmentation based on ant colony[J]. High Power Laser and Particle Beams, 2010, 22(05): 0- .
[16]	chi fangting, li bo, liu yiyang, chen sufen, jiang bo. Ultraviolet light and ozone surface modification of poly-alpha α-methylstyrene using electroless nickel plating[J]. High Power Laser and Particle Beams, 2009, 21(03): 0- .
[17]	qin hanlin, zhou huixin, liu shangqian, li fan. Dim and small target background suppression using bilateral filtering[J]. High Power Laser and Particle Beams, 2009, 21(01): 0- .
[18]	liu chang-song, yan gao-shi, cai jian-rong. A new search algorithm for fast block-matching motion estimation[J]. High Power Laser and Particle Beams, 2007, 19(10): 0- .
[19]	fang liang, lu jia-jia, ye yu-tang, yang xian-ming, cheng zhi-qiang. New method for edge detection of infrared images based on Mumford-Shah model[J]. High Power Laser and Particle Beams, 2007, 19(04): 0- .
[20]	cheng yong-dong, li jia-yin. Study on microwave self-adapting equalizer for amplifier of multiple-beam klystron[J]. High Power Laser and Particle Beams, 2002, 14(01): 0- .

Supplements(0)

Cited By

Cited by

Periodical cited type(3)

1.	潘强，印鉴. 基于权重约束决策的图像增强算法. 控制工程. 2018(11): 2017-2021 .
2.	王新赛，周丰俊，郑磊，贺菁. 红外成像技术在城市地下空间防灾监测与应急搜救中的应用发展对策. 中国工程科学. 2017(06): 92-99 .
3.	秦翰林，曾庆杰，李佳，周慧鑫，延翔，韩姣姣，吕恩龙. 奇异值非线性修正的低对比度红外图像实时增强. 强激光与粒子束. 2015(01): 59-62 . 本站查看