基于低秩和重加权稀疏表示的红外弱小目标检测算法  

Infrared Dim and Small Target Detection Algorithm Based on Low-Rank and Reweighted Sparse Representation

作　　者：杨亚东 黄胜一 谭毅华[1] YANG Yadong;HUANG Shengyi;TAN Yihua(School of Articial Intelligence and Automation,Huazhong University of Science and Technology,Wuhan 430074,Hubei,China)

机构地区：[1]华中科技大学人工智能与自动化学院,湖北武汉430074

出　　处：《应用科学学报》2023年第5期753-765,共13页Journal of Applied Sciences

基　　金：国家自然科学基金(No.41371339);中央高校基本科研业务费专项(No.2017KFYXJJ179)资助。

摘　　要：红外弱小目标检测技术是红外告警系统中的关键技术之一,但如何精确、快速、鲁棒地进行弱小目标检测依然是个难题。该文提出了基于低秩和重加权稀疏表示的红外弱小目标检测算法,设计了新的优化方程,更精确地描述了背景矩阵的秩,利用结构张量提取红外图像的局部先验信息权重,同时提取目标矩阵的自增强稀疏权重,使模型能够更好地抑制背景中的边缘干扰来提取目标。实验表明:所提算法精度优于现有的经典基线算法,速度超越了一些经典算法。从性能和时间两个方面综合考虑,所提算法有着较好的优越性,对远距离红外弱小目标告警具有积极的意义和良好的应用价值。The detection of infrared dim and small targets is one of the key technologies in the infrared warning system.It remains challenging to accurately,quickly,and robustly de-tect dim and small targets.This paper proposes an infrared dim and small target detection algorithm based on low-rank and reweighted sparse representation.The algorithm formu-lates a new optimization equation to more accurately describe the rank of the background matrix and utilizes the structure tensor to extract local prior information.Experimental results show that the proposed algorithm improves the accuracy,speed,and robustness of detecting dim and small infrared targets.

关 键 词：小目标检测 红外图像 矩阵分解 低秩稀疏表示 

分 类 号：P751.1[交通运输工程—港口、海岸及近海工程]