基于加权块稀疏联合非凸低秩约束的高光谱图像去条带方法  

Hyperspectral images destriping approach with weighted block sparsity regularization and non-convex low-rank penalty

作　　者：袁宇丽[1] 吕俊瑞 罗学刚 YUAN Yuli;LYU Junrui;LUO Xuegang(College of Computer Science,Neijiang Normal College,Neijiang 641100,China;School of Mathematics and Computer Science,Panzhihua University,Panzhihua 617000,China;School of Information and Engineering,Sichuan Tourism University,Chengdu 610100,China;School of Communication and Information Engineering,University of Electronic Science and Technology of China,Chengdu 610054,China)

机构地区：[1]内江师范学院计算机科学学院,四川内江641100 [2]攀枝花学院数学与计算机学院,四川攀枝花617000 [3]四川旅游学院信息与工程学院,四川成都610100 [4]电子科技大学信息与通信工程学院,四川成都610054

出　　处：《应用光学》2021年第2期283-291,共9页Journal of Applied Optics

基　　金：四川省科技创新基金项目(2019088),四川省教育厅科研项目(18ZA0288)。

摘　　要：针对高光谱图像(hyperspectral images,HSI)去条带易引起影像结构细节丢失问题,提出一种基于加权块稀疏(weighted block sparsity,WBS)正则化联合最小最大非凸惩罚(minimax concave penalty,MCP)约束的HSI去条带方法。本算法采用加权ℓ_(2),1范数和MCP范数对条带稀疏结构和低秩约束,ℓ_(1)范数对干净图像结构保持正则化约束,构建加权块稀疏和MCP约束的条带去除模型,采用交替方向乘子(alternating direction method of multipliers,ADMM)算法迭代求解对应模型,重建获得干净的HSI图像。实验结果表明,提出方法在实际HSI的平均等效视数从28.45提高到83.47,边缘保持指数较其他算法至少增加0.056,特别是对于非周期条带噪声,采用自适应权值更新稀疏水平,增强了组稀疏性,在保持影像边缘和加强区域平滑性方面性能更佳,去噪声效果更好。To solve the loss of image details when removing the stripe noise of hyperspectral images(HSI),a destriping method for hyperspectral images was proposed,which was based on the weighted block sparsity(WBS)regularization and the minimax concave penalty(MCP)constraint(WBS-MCP).WBS-MCP was constructed by employing weightedℓ_(2),1 norm and MCP norm for the sparse features and low-rank constraints,and applyingℓ_(1) norm to the edge smoothing constraint of clean image.The alternating direction method of multipliers(ADMM)algorithm was used to iteratively solve the corresponding constraint model,and the clean HSI image was obtained by reconstruction.The experimental results show that the mean equivalent number of look of real HSI improves from 28.45 to 83.47,and the edge retention index increased by 0.056 at least,especially for the aperiodic stripe noise.The adaptive weight was used to update the sparse level,which could enhance the group sparsity and has better performance in maintaining the image edge and enhancing the area smoothness,so that the noise removal has better effect on aperiodic stripe noise.

关 键 词：加权块稀疏 最小最大非凸惩罚 高光谱图像条带噪声 

分 类 号：TN957.54[电子电信—信号与信息处理] TP391[电子电信—信息与通信工程]