基于多尺度局部对比度和多尺度梯度一致性的红外小弱目标检测算法

doi:10.3969/j.issn.1000-1093.2018.08.009

兵工学报 ›› 2018, Vol. 39 ›› Issue (8): 1526-1535.doi: 10.3969/j.issn.1000-1093.2018.08.009

基于多尺度局部对比度和多尺度梯度一致性的红外小弱目标检测算法

刘德鹏¹，李正周^1,2,3,4，曾靖杰¹，熊伟奇¹，亓波^3,4

(1.重庆大学通信工程学院，重庆 400044；2.重庆大学信息物理社会可信服务计算教育部重点实验室，重庆 400044；3.中国科学院光电技术研究所，四川成都 610209；4.中国科学院光束控制重点实验室，四川成都 610209)

收稿日期:2017-11-08 修回日期:2017-11-08 上线日期:2018-09-21
通讯作者: 李正周（1974—），男，教授，博士生导师 E-mail:lizhengzhou@cqu.edu.cn
作者简介:刘德鹏（1989—），男，博士研究生。E-mail：depeng@cqu.edu.cn
基金资助:
国家自然科学基金项目(61675036)；重庆基础与前沿研究计划项目(CSTC2016JCYJA0193)；中国科学院光束控制重点实验室基金项目(2017LBC006)

Infrared Dim Small Target Detection Based on Multi-scale Local Contrast and Multi-scale Gradient Coherence

LIU De-peng¹， LI Zheng-zhou^1,2,3,4， ZENG Jing-jie¹， XIONG Wei-qi¹， QI Bo^3,4

(1.College of Communication Engineering，Chongqing University，Chongqing 400044，China；2.Key Laboratory of Dependable Service Computing in Cyber Physical Society of Ministry of Education，Chongqing University，Chongqing 400044，China；3.Institute of Optics and Electronics，Chinese Academy of Sciences，Chengdu 610209，Sichuan，China；4.Key Laboratory of Beam Control，Chinese Academy of Sciences，Chengdu 610209，Sichuan，China)

Received:2017-11-08 Revised:2017-11-08 Online:2018-09-21

摘要/Abstract

摘要： 针对复杂背景和强杂波干扰下红外小弱目标检测虚警率高的问题，提出了一种基于多尺度局部对比度方法与多尺度梯度一致性方法的红外小弱目标检测算法。利用多尺度局部对比度方法对红外图像中红外小弱目标进行增强，利用多尺度梯度一致性方法剔除复杂背景和强杂波干扰造成的虚警。从信噪比（SNR）增益、平均残留背景绝对值、检测率、虚警率及ROC曲线方面将新算法与max-mean算法、max-median算法、top-hat算法、IPI算法及MGDWIE算法进行了对比。实验显示：新算法相较于对比算法具有更高的SNR增益、更低的平均残留背景绝对值、更高的检测率及更低的虚警率。对比结果表明：新算法在复杂背景和强杂波干扰下具有良好的红外小弱目标检测准确性和鲁棒性，有效改善了复杂背景和强杂波干扰下红外小弱目标检测虚警率高的问题。

关键词: 红外小弱目标检测, 多尺度局部对比度, 多尺度梯度一致性, 红外图像

Abstract: A novel algorithm based on multi-scale local contrast method and multi-scale gradient coherence method is proposed for the high false alarm of infrared dim small target caused by complex background and heavy clutter. The multi-scale local contrast method is used to enhance the infrared dim small target in infrared image, and the multi-scale gradient coherence method is used to avoid the false alarms caused by complex background and heavy clutter. The proposed algorithm is compared with max-mean, max-median, top-hat, IPI and MGDWIE algorithms in terms of signal-to-noise ratio（SNR）gain, mean absolute value of residual background, detectivity and false alarm rate. The proposed algorithm achieves higher SNR gain, lower mean absolute value of residual background, higher detectivity and lower false alarm rate compared to the baseline algorithms. The experimental results show that the proposed algorithm can effectively reduce the false alarm rate under the disturbance of complex background and heavy clutter.Key

Key words: infrareddimsmalltargetdetection, multi-scalelocalcontrast, multi-scalegradientcoherence, infraredimage

中图分类号:

TP391.413

刘德鹏，李正周，曾靖杰，熊伟奇，亓波. 基于多尺度局部对比度和多尺度梯度一致性的红外小弱目标检测算法[J]. 兵工学报, 2018, 39(8): 1526-1535.

LIU De-peng， LI Zheng-zhou， ZENG Jing-jie， XIONG Wei-qi， QI Bo. Infrared Dim Small Target Detection Based on Multi-scale Local Contrast and Multi-scale Gradient Coherence[J]. Acta Armamentarii, 2018, 39(8): 1526-1535.

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基于多尺度局部对比度和多尺度梯度一致性的红外小弱目标检测算法

Infrared Dim Small Target Detection Based on Multi-scale Local Contrast and Multi-scale Gradient Coherence

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