局部对比度融合重加权的红外图像小目标检测  

Combining Local Contrast with Reweighting for Small Target Detection in Infrared Images

作　　者：霍贝祺 陈文东 杨赟秀[3] 刘星[1] 舒勤[1] HUO Beiqi;CHEN Wendong;YANG Yunxiu;LIU Xing;SHU Qin(College of Electrical Engineering,Sichuan University,Chengdu 610000,China;Beijing Institute of Remote Sensing Equipment,Beijing 100000,China;Southwest Institute of Technical Physics,Chengdu 610000,China)

机构地区：[1]四川大学电气工程学院,成都610000 [2]北京遥感设备研究所,北京100000 [3]西南技术物理研究所,成都610000

出　　处：《电光与控制》2025年第1期27-33,共7页Electronics Optics & Control

基　　金：国家自然科学基金(62171387)。

摘　　要：红外图像块小目标检测算法在数据链、预警、制导等领域有较为广泛的应用,如利用数据链可以将红外图像块小目标精确传送给雷达。为了在复杂背景条件下进一步提高红外小目标检测的效果,提出了一种重加权和局部先验融合的红外图像块(IPI)小目标检测算法。首先,采用加权Schatten p范数对背景图像块进行约束;其次,引入局部对比度先验信息和加权l_(1)范数抑制非目标稀疏点,进一步增强目标图像稀疏性,使算法模型性能进一步得到提升。仿真结果表明,所提算法在抑制背景杂波和精确检测目标方面均有较好的结果,优于现有经典算法。The Infrared Patch-Image(IPI)small target detection method has a wide range of applications in the fields of data chain,early warning,guidance,etc.For example,data chain can be used to accurately transmit IPI of small targets to the radar.In order to further improve the effects of infrared small target detection under complex background conditions,this paper proposes an IPI small target detection algorithm combining reweighting with local a priori.Firstly,the weighted Schatten p norm is used to constrain the background patch image.Secondly,the prior information of local contrast and the weighted l_(1)norm are introduced to suppress sparse non-target points,which further enhances the sparsity of the target image and further improves the performance of the algorithm model.The simulation results show that the proposed algorithm has better results than the existing classic algorithms in background clutter suppressing and accurate target detecting.

关 键 词：目标检测 红外小目标 稀疏低秩分解 红外图像块 

分 类 号：TP751.1[自动化与计算机技术—检测技术与自动化装置]