基于二维模态分解与斑块对比度相结合的红外小目标检测算法  

Infrared small target detection algorithm based on two-dimensional modal decomposition combined with patch contrast

作　　者：徐丹旸 魏宏光 金秋春[2] 钱金旺 郭星辰 Xu Danyang;Wei Hongguang;Jin Qiuchun;Qian Jinwang;Guo Xingchen(Electrical Engineering College,Henan University of Science and Technology,Luoyang 471000,China;School of Intelligent Engineering,Zhengzhou University of Aeronautics,Zhengzhou 450015,China)

机构地区：[1]河南科技大学电气工程学院,河南洛阳471000 [2]郑州航空工业管理学院智能工程学院,河南郑州450015

出　　处：《红外与激光工程》2022年第12期372-378,共7页Infrared and Laser Engineering

基　　金：国家自然科学基金民航联合基金重点项目(U1833203);航空科学基金(2020Z019055001);河南科技攻关项目(202102210330);河南科技攻关项目(222102210136);郑州航空工业管理学院研究生教育创新计划基金(2021CX48)。

摘　　要：红外小目标检测在红外目标搜索跟踪等应用中发挥着重要作用。文中提出一种二维经验模态分解与多尺度斑块对比度算法相结合的红外小目标检测算法。首先,利用二维经验模态分解将红外图像分解成不同尺度的模态分量,再将低频模态分量去掉进行图像重构,实现对背景杂波的抑制。然后,将重构图像做为多尺度斑块对比度算法的输入,生成目标结果图。最后,对目标结果图进行自适应阈值分割,检测出真实的红外小目标。实验仿真结果表明,该算法与现有算法相比,在不同背景下能够有效抑制背景对目标的干扰,具有较高的检测率,验证了该算法的有效性和鲁棒性。Infrared small target detection plays an important role in applications such as infrared target search and tracking.In this paper,we propose an infrared small target detection algorithm combining two-dimensional empirical modal decomposition and multi-scale patch contrast algorithm.First,the infrared image is decomposed into modal components of different scales using two-dimensional empirical modal decomposition,and then the low-frequency modal components are removed for image reconstruction to achieve the suppression of background clutter.Then,the reconstructed image is used as the input of the multi-scale patch contrast algorithm to generate the target result map.Finally,adaptive threshold segmentation is performed on the target result map to detect the real infrared small targets.The experimental simulation results show that the algorithm can effectively suppress the background interference to the target with high detection rate under different backgrounds compared with the existing algorithms,which verifies the effectiveness and robustness of the algorithm.

关 键 词：红外小目标检测 二维经验模态分解 模态分量 多尺度斑块对比度 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]