Hyperspectral image denoising using the robust low-rank tensor recovery.
Hyperspectral image denoising using the robust low-rank tensor recovery.
复制标题
DOI：
10.1364/josaa.32.001604
复制
发表时间：
2015-09
期刊：
影响因子：
--
通讯作者：
Chang Li;Yong Ma;Jun Huang;Xiaoguang Mei;Jiayi Ma
中科院分区：
文献类型：
--
作者：
Chang Li;Yong Ma;Jun Huang;Xiaoguang Mei;Jiayi Ma
去噪是对高光谱图像进行深入分析的重要预处理步骤，目前已有多种去噪方法用于高光谱数据立方体的去噪。然而，传统的去噪方法对野值和非高斯噪声敏感。本文利用干净HSI数据的低秩张量特性以及异常值和非高斯噪声的稀疏性，提出了一种基于鲁棒低秩张量恢复的新模型，该模型可以保持HSI的全局结构，同时去除异常值和不同类型的噪声：该模型可以用非精确增广拉格朗日方法求解，仿真和真实的高光谱图像实验结果表明，该方法对HSI去噪是有效的。
Denoising is an important preprocessing step to further analyze the hyperspectral image (HSI), and many denoising methods have been used for the denoising of the HSI data cube. However, the traditional denoising methods are sensitive to outliers and non-Gaussian noise. In this paper, by utilizing the underlying low-rank tensor property of the clean HSI data and the sparsity property of the outliers and non-Gaussian noise, we propose a new model based on the robust low-rank tensor recovery, which can preserve the global structure of HSI and simultaneously remove the outliers and different types of noise: Gaussian noise, impulse noise, dead lines, and so on. The proposed model can be solved by the inexact augmented Lagrangian method, and experiments on simulated and real hyperspectral images demonstrate that the proposed method is efficient for HSI denoising.