[1]刘 旭,崔文楠.采用人类视觉对比机制的红外弱小目标检测[J].红外技术,2020,42(6):559-565.[doi:doi:10.11846/j.issn.1001_8891.202006008]
 LIU Xu,CUI Wennan.Infrared-Image-Based Detection of Dim and Small Targets Using Human Visual Contrast Mechanism [J].Infrared Technology,2020,42(6):559-565.[doi:doi:10.11846/j.issn.1001_8891.202006008]
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采用人类视觉对比机制的红外弱小目标检测
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《红外技术》[ISSN:1001-8891/CN:CN 53-1053/TN]

卷:
42卷
期数:
2020年第6期
页码:
559-565
栏目:
出版日期:
2020-06-23

文章信息/Info

Title:
Infrared-Image-Based Detection of Dim and Small Targets
Using Human Visual Contrast Mechanism
文章编号:
1001-8891(2020)06-0559-07
作者:
刘 旭123崔文楠12
1. 中国科学院 智能红外感知重点实验室,上海 200083;2. 中国科学院 上海技术物理研究所,上海 200083;
3. 中国科学院大学,北京 100049
Author(s):
LIU Xu123CUI Wennan12
1. Key Laboratory of Intelligent Infrared Perception, Chinese Academy of Sciences, Shanghai 200083, China;
2. Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China;
3. University of Chinese Academy of Sciences, Beijing 100049, China
关键词:
弱小目标检测高斯函数差分滤波器局部对比度方法红外图像
Keywords:
dim and small target detection difference of Gaussians local contrast method infrared image
分类号:
TP751.1
DOI:
doi:10.11846/j.issn.1001_8891.202006008
文献标志码:
A
摘要:
针对复杂背景下红外弱小目标检测难题,提出一种基于人类视觉系统对比机制的红外弱小目标检测算法。首先,对红外图像进行预处理,通过中值滤波去除红外图像中的孤立噪声点。然后对处理后的图像进行高斯函数差分滤波处理,抑制图像中大面积高亮区域。最后,通过改进的基于局部对比度方法去除高亮边缘区域,消除高疑似目标,最终实现对复杂背景下红外弱小目标的检测。实验表明:相较于传统的LCM算法、Top-hat算法、TDLMS算法和Infrared Patch-Image Model算法等,该算法在虚警率、正确检测率、检测时间等方面更有优势,具有检测率高、虚警率低、鲁棒性好、运行时间短的特点。
Abstract:
In this paper, an infrared-image-based algorithm is proposed for the detection of dim and small targets in complex backgrounds. The proposed algorithm is based on the contrast mechanism of the human visual system. First, an infrared image was preprocessed, and isolated noise points in the image were removed via median filtering. The processed image was then subjected to difference-of-Gaussians filtering to suppress large-area highlighted areas in the image. Finally, an improved local contrast algorithm was used to remove the highlighted edge regions and eliminate the high suspect target to achieve the detection of dim and small targets in complex backgrounds using infrared images. Experimental results show that compared with the traditional LCM algorithm, top-hat algorithm, TDLMS algorithm, and infrared patch-image model, the proposed algorithm is more advantageous with regard to the false alarm rate, correct detection rate, detection time, etc. It also has the characteristics of a high detection rate, low false alarm rate, good robustness, and short running time.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2019-08-01;修订日期:2019-09-18.
作者简介:刘旭(1994-),女,黑龙江人,硕士,主要从事红外图像处理方面的研究工作。E-mail:lxulxu1994@163.com。
通信作者:崔文楠(1979-),男,辽宁人,博士,主要从事红外成像与仿真方面的研究工作。E-mail:cuiwennan@mail.sitp.ac.cn。
更新日期/Last Update: 2020-06-22