基于稀疏增强重加权与掩码块张量的红外弱小目标检测  

Infrared Dim Target Detection Based on Sparse Enhanced Reweighting and Mask Patch-tensor

作　　者：孙尚琦 张宝华[1] 李永翔 吕晓琪 谷宇[1] 李建军[1] SUN Shangqi;ZHANG Baohua;LI Yongxiang;LYU Xiaoqi;GU Yu;LI Jianjun(School of Information Engineering,Inner Mongolia University of Science and Technology,Baotou 014010,China;School of Energy and Transportation Engineering,Inner Mongolia Agricultural University,Huhehaote 010018,China;School of Information Engineering,Mongolia Industrial University,Huhehaote 010051,China)

机构地区：[1]内蒙古科技大学信息工程学院,内蒙古包头014010 [2]内蒙古农业大学能源与交通工程学院,内蒙古呼和浩特010018 [3]内蒙古工业大学信息工程学院,内蒙古呼和浩特010051

出　　处：《红外技术》2024年第3期305-313,共9页Infrared Technology

基　　金：国家自然科学基金项目(61841204,61962046,62001255,62066036,62262048);内蒙古杰青培育项目(2018JQ02);内蒙古科技计划项目(2020GG0315,2021GG0082);中央引导地方科技发展资金项目(2021ZY0004);内蒙古草原英才,内蒙古自治区自然科学基金(2022MS06017,2018MS06018,2019MS06003);教育部“春晖计划”合作科研项目(教外司留1383号);内蒙古自治区高等学校科学技术研究项目(NJZY145)资助。

摘　　要：高度异构的复杂背景破坏了场景的低秩性,现有算法难以利用低秩稀疏恢复方法从背景中分离出小目标。为了解决上述问题,本文将小目标检测问题转化为张量模型的凸优化函数求解问题,提出基于稀疏增强重加权与掩码块张量的检测模型。首先,将掩码块图像以堆叠方式扩展至张量空间,并构建掩码块张量模型以筛选候选目标。在此基础上,利用结构张量构建稀疏增强重加权模型以抑制背景杂波,克服凸优化函数求解过程中设定加权参数的缺陷。实验表明本文检测算法在背景抑制因子及信杂比增益两方面都优于新近代表性算法,证明该算法的有效性。The high heterogeneity of complex backgrounds destroys the low rank of a scene,and it is difficult for existing algorithms to use low-rank sparse recovery methods to separate dim targets from the background.To resolve this problem,this study transforms the dim target detection problem into a convex optimization function-solving problem for tensor models.It proposes a detection model based on sparsely enhanced reweighting and mask patch tensors.First,the stacked mask patch image was expanded into a tensor space,and a mask patch-tensor model was constructed to filter the candidate targets.Thus,a sparse enhanced reweighting model was constructed using structural tensors to suppress background clutter,and the limitation of setting the weighting parameters can be overcome by solving convex optimization functions.The experiments show that the proposed algorithm outperforms recent representative algorithms regarding the background suppression factor and signal-to-noise ratio gain,demonstrating its effectiveness.

关 键 词：小目标检测 低秩稀疏恢复 掩码块张量 稀疏增强重加权 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] TN215[自动化与计算机技术—计算机科学与技术]