张量分解和自适应图全变分的高光谱图像去噪

蔡明娇; 蒋俊正; 蔡万源; 周芳

doi:10.19665/j.issn1001-2400.20230412

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张量分解和自适应图全变分的高光谱图像去噪

计算机科学与技术&网络空间安全 | 更新时间：2024-06-03

- 张量分解和自适应图全变分的高光谱图像去噪
- Hyperspectral image denoising based on tensor decomposition and adaptive weight graph total variation
- 西安电子科技大学学报 2024年51卷第2期页码：157-169
- 作者机构：
  
  1. 桂林电子科技大学信息与通信学院,广西壮族自治区桂林 541004
  2. 桂林电子科技大学卫星导航定位与位置服务国家地方联合工程研究中心,广西壮族自治区桂林 541004
  3. 桂林电子科技大学广西无线宽带通信与信号处理重点实验室,广西壮族自治区桂林 541004
- 作者简介：
  
  [ "蔡明娇(1997—),女,桂林电子科技大学硕士研究生,E-mail:[email protected]; " ]
  [ "蒋俊正(1983—),男,教授,E-mail:[email protected]; " ]
  [ "蔡万源(1998—),男,桂林电子科技大学硕士研究生,E-mail:[email protected]" ]
  周芳(1984—),女,副教授,E-mail:[email protected]
- 基金信息：
  
  国家自然科学基金(62171146);国家自然科学基金(62261014);广西创新驱动发展专项(桂科AA21077008);广西自然科学杰出青年基金(2021GXNSFFA220004);广西科技基地和人才专项(桂科AD21220112);广西无线宽带通信与信号处理重点实验室主任基金(GXKL06220107);桂林电子科技大学研究生教育创新计划(2022YCXS039)
- DOI：10.19665/j.issn1001-2400.20230412
  中图分类号： TP751
- 纸质出版日期：2024-4-20，
  
  网络出版日期：2023-9-6，
  
  收稿日期：2023-2-23，
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蔡明娇, 蒋俊正, 蔡万源, 等. 张量分解和自适应图全变分的高光谱图像去噪[J]. 西安电子科技大学学报, 2024,51(2):157-169.

Mingjiao CAI, Junzheng JIANG, Wanyuan CAI, et al. Hyperspectral image denoising based on tensor decomposition and adaptive weight graph total variation[J]. Journal of Xidian University, 2024,51(2):157-169.
蔡明娇, 蒋俊正, 蔡万源, 等. 张量分解和自适应图全变分的高光谱图像去噪[J]. 西安电子科技大学学报, 2024,51(2):157-169. DOI： 10.19665/j.issn1001-2400.20230412.

Mingjiao CAI, Junzheng JIANG, Wanyuan CAI, et al. Hyperspectral image denoising based on tensor decomposition and adaptive weight graph total variation[J]. Journal of Xidian University, 2024,51(2):157-169. DOI： 10.19665/j.issn1001-2400.20230412.

摘要

高光谱图像在采集过程中受到观测条件、成像仪材料属性、传输条件等客观因素的影响

不可避免地会引入各种噪声。这严重降低了高光谱图像的质量以及限制了后续处理的精度。因此

高光谱图像去噪是一个极其重要的预处理步骤。针对高光谱图像去噪问题

提出了低秩张量分解和自适应图全变分的高光谱图像去噪算法。首先

利用低秩张量分解来描述高光谱图像的全局空间和光谱相关性

并使用自适应权重图全变分来刻画高光谱图像空间维度上的分段平滑特性和保留高光谱图像的边缘信息;此外

采用

-范数、Frobenius-范数分别刻画包括条纹噪声、脉冲噪声、死线噪声在内的稀疏噪声和高斯噪声。由此高光谱图像去噪问题归结为一个包含低秩张量分解和自适应图全变分的约束优化问题。利用增广拉格朗日乘子法对该优化问题进行交替求解。实验结果表明

所提出的高光谱图像去噪算法与现有的算法相比

能够充分刻画高光谱图像数据的内在结构特性

具有更好的去噪性能。

Abstract

During the acquisition process of hyperspectral images

various noises are inevitably introduced due to the influence of objective factors such as observation conditions

material properties of the imager

and transmission conditions

which severely reduces the quality of hyperspectral images and limits the accuracy of subsequent processing.Therefore

denoising of hyperspectral images is an extremely important preprocessing step.For the hyperspectral image denoising problem

a denoising algorithm

which is based on low-rank tensor decomposition and adaptive weight graph total variation regularization named LRTDGTV

is proposed in this paper.Specifically

Low-rank tensor decomposition is used to cha

racterize the global correlation among all bands

and adaptive weight graph total variation regularization is adopted to characterize piecewise smoothness property of hyperspectral images in the spatial domain and preserve the edge information of hyperspectral images.In addition

sparse noise

including stripe noise

impulse noise and deadline noise

and Gaussian noise are characterized by l

-norm and Frobenius-norm

respectively.Thus

the denoising problem can be formulated into a constrained optimization problem involving low-rank tensor decomposition and adaptive weight graph total variation regularization

which can be solved by employing the augmented Lagrange multiplier(ALM) method.Experimental results show that the proposed hyperspectral image denoising algorithm can fully characterize the inherent structural characteristics of hyperspectral images data and has a better denoising performance than the existing algorithms.

关键词

高光谱图像去噪Tucker分解自适应图全变分

Keywords

hyperspectral image denoisingtucker decompositionadaptive weight graph total variation

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