系数矩阵
奇异值分解
算法
正规化(语言学)
计算机科学
秩(图论)
稀疏矩阵
矩阵分解
基质(化学分析)
全变差去噪
低秩近似
计算复杂性理论
数学
数学优化
人工智能
汉克尔矩阵
降噪
高斯分布
数学分析
特征向量
物理
材料科学
量子力学
组合数学
复合材料
作者
Ting Liu,Jungang Yang,Boyang Li,Yingqian Wang,Wei An
出处
期刊:IEEE Transactions on Geoscience and Remote Sensing
[Institute of Electrical and Electronics Engineers]
日期:2023-01-01
卷期号:61: 1-18
标识
DOI:10.1109/tgrs.2023.3324821
摘要
Low-rank and sparse decomposition based models are powerful and robust tools for infrared small target detection. However, due to the calculation of singular value decomposition (SVD) and the optimization of complex regularization terms, existing low-rank models often suffer from high computational complexity. To solve this problem, based on the theorem that representative coefficient matrix obtained by orthogonal transformation of data matrix can inherit the spatial structure of data matrix, we propose a representative coefficient total variation (RCTV) method for efficient infrared small target detection. In our method, we use total variational to constraint representative coefficient matrix instead of data matrix to describe local smooth prior, which helps remove noise and reduce computational complexity. Meanwhile, we control the number of columns in the representative coefficient matrix to maintain the low-rank characteristics of background, which avoids SVD calculation and improves detection efficiency. Therefore, the RCTV regularization can simultaneously describe local smooth prior and low-rank prior. Moreover, to better enhance the sparsity of targets and distinguish sparse non-target points, we use the log-sum function to adaptively assign weights to targets. It helps obtain more accurate detection performance. The proposed model is efficiently solved by the alternating direction multiplier method (ADMM). A large number of experiments show that the proposed method is superior to existing low-rank methods in both detection accuracy and efficiency.
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