摘要
高光谱图像在采集过程中经常受到混合噪声的干扰,严重影响了图像后续应用的性能,因此图像去噪已成为一个极其重要的预处理过程。文中采用非凸正则项代替传统的核范数重新构造逼近问题,使稀疏正则项更贴近本质秩函数的属性,进而提出了一种将非凸代理函数、全变分正则项和l 2,1范数集成于统一框架的混合噪声去除算法。所提算法旨在将退化的高光谱图像以矩阵的形式分解为低秩分量和稀疏项,并利用全变分正则化保持边缘信息,提高了高光谱图像的空间分段平滑性。最后利用非凸代理函数的特殊性质,采用一种基于增广拉格朗日乘子法的迭代算法进行变量优化求解。通过多组实验进行验证,结果表明所提算法不仅能有效地去除混合噪声,而且能较好地保持图像的结构和细节,与现有的其他高光谱去噪方法相比,其在视觉效果和定量评价结果上都明显提升。
Hyperspectral images(HSIs)are often interfered by hybrid noise in the acquisition process,which seriously weakens the performance of subsequent applications of HSIs.In this paper,nonconvex regularizer is used to reconstruct the approximation problem instead of the traditional nuclear norm,which guarantees a tighter approximation of the original sparsity constrained rank function.Then a hybrid noise removal model integrating nonconvex surrogate function,total variation regularization and l 2,1 norms together into a unified framework is proposed.The proposed algorithm aims to decompose the degraded HSIs into low rank components and sparse terms in the matrix mode,and uses total variation regularization to maintain edge information and improve the spatial piecewise smoothness of the HSIs.Finally,using the special properties of nonconvex surrogate function,an iterative algorithm based on augmented Lagrangian multiplier method is used for optimization.Extensive experiments on several well-known datasets are conducted for model evaluation,and the results show that the proposed algorithm can not only effectively remove hybrid noise,but also can better maintain the structure and details of the images.Compared with other existing hyperspectral denoising methods,the visual effects and quantitative evaluation results of the proposed algorithm are significantly better.
作者
陶星朋
徐宏辉
郑建炜
陈婉君
TAO Xing-peng;XU Hong-hui;ZHENG Jian-wei;CHEN Wan-jun(School of Computer Science and Technology,Zhejiang University of Technology,Hangzhou 310023,China)
出处
《计算机科学》
CSCD
北大核心
2021年第8期125-133,共9页
Computer Science
基金
国家重点研发计划项目(2018YFE0126100)
国家自然科学基金(61602413)
浙江省自然科学基金(LY19F030016)
浙江省实验室开放研究项目(2019KD0AD01/007)
国家卫生委员会科研基金(WKJ-ZJ-2102)
浙江省教育厅项目(Y201941027)。
关键词
高光谱图像
混合噪声
全变分
非凸正则项
增广拉格朗日乘子法
Hyperspectral image
Hybrid noise
Total variation
Nonconvex regularizer
Augmented lagrangian multipliers
