基于加权RPCA的单帧红外图像小目标检测
Single-Frame Infrared Image Small Target Detection Based on Weighted RPCA
DOI: 10.12677/CSA.2021.1112303, PDF,   
作者: 杨晓潇, 付小宁：西安电子科技大学，陕西 西安
关键词: 小目标检测；加权RPCA；块图像；Small Target Detection； Weighted RPCA； Block Image
摘要: 红外图像目标检测作为红外探测系统的关键技术。本文提出一种改进的基于加权鲁棒性主成分分析的单帧红外小目标图像检测算法。该方法首先建立红外图像的小目标模型，其中包含目标、背景和噪声及杂波；其次将红外图像构造成块图像，转换为RPCA问题；采用加权鲁棒性主成分分析算法进行求解，将目标矩阵和背景矩阵进行分离，并通过图像重构得到目标图像和背景图像。使用包含天空、海洋和沙漠不同背景下的单帧红外小目标数据集验证了本文提出算法的有效性。
Abstract: Infrared image target detection is the key technology of infrared detection system. This paper proposes an improved single-frame infrared small target image detection algorithm based on weighted robust principal component analysis. Firstly, this method establishes a small target model of the infrared image, which contains the target, background, noise and clutter; secondly, the infrared image is constructed into a block image and converted to the RPCA problem; finally, the weighted robust principal component analysis algorithm is used to solve the problem, and the target matrix and the background matrix are separated, and the target image and the background image are obtained through image reconstruction. Using single-frame infrared small target datasets under different backgrounds of sky, ocean and desert verifies the effectiveness of the proposed algorithm.
文章引用：杨晓潇, 付小宁. 基于加权RPCA的单帧红外图像小目标检测[J]. 计算机科学与应用, 2021, 11(12): 3001-3011. https://doi.org/10.12677/CSA.2021.1112303

参考文献

[1] 薛国姣. 红外小目标显著性特征提取方法研究[D]: [硕士学位论文]. 西安: 西安电子科技大学, 2018.
[2] 樊俊良, 高永明, 吴止锾, 等. 基于RPcA的单帧红外小目标检测算法[J]. 兵器装备工程学报, 2018, 39(11): 147-151.
[3] 李俊宏, 张萍, 王晓玮, 黄世泽. 红外弱小目标检测算法综述[J]. 中国图象图形学报, 2020, 25(9): 1739-1753.
[4] 王忠美, 杨晓梅, 顾行发. 基于鲁棒主成分分析的红外图像小目标检测[J]. 兵工学报, 2016, 37(9): 8.
[5] 杨国亮, 王艳芳, 丰义琴, 等. 基于加权RPCA的非局部图像去噪方法[J]. 计算机工程与设计, 2015, 36(11): 6.
[6] 张丛丛. 基于背景低秩与目标稀疏特性的红外弱小目标检测方法研究[D]: [硕士学位论文]. 南京: 南京理工大学, 2018.
[7] 王艳芳. 基于改进RPCA的非局部图像去噪算法研究[D]: [硕士学位论文]. 赣州:江西理工大学, 2016.
[8] Gu, S., Zhang, L., Zuo, W., et al. (2014) Weighted Nuclear Norm Minimization with Applica-tion to Image Denoising. 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, 23-28 June 2014, 2862-2869. [Google Scholar] [CrossRef] 
[9] Gao, C., Meng, D., Yang, Y., Wang, Y., Zhou, X. and Hauptmann, A.G. (2013) Infrared Patch-Image Model for Small Target Detection in a Single Image. IEEE Transactions on Image Processing, 22, 4996-5009. [Google Scholar] [CrossRef] 
[10] 杨丽萍, 冯晓毅. 一种基于背景预测的红外弱小目标检测方法[J]. 红外技术, 2007, 29(7): 404-408.
[11] Gu, S., Xie, Q., Meng, D., et al. (2017) Weighted Nuclear Norm Minimiza-tion and Its Applications to Low Level Vision. International Journal of Computer Vision, 121, 183-208. [Google Scholar] [CrossRef]