| 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 |
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| 引用本文： | 袁宇丽,吕俊瑞,罗学刚. 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[J]. 应用光学, 2021, 42(2): 283-291. DOI: 10.5768/JAO202142.0202005 |
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| 作者姓名： | 袁宇丽 吕俊瑞 罗学刚 |
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| 作者单位： | 1.内江师范学院 计算机科学学院，四川 内江 641100 |
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| 基金项目： | 四川省科技创新基金项目（2019088），四川省教育厅科研项目（18ZA0288） |
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| 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?2,1范数和MC...
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| 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 |
| 收稿时间： | 2020-08-07 |
Hyperspectral images destriping approach with weighted block sparsity regularization and non-convex low-rank penalty |
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| Affiliation: | 1.College of Computer Science，Neijiang Normal College，Neijiang 641100，China2.School of Mathematics and Computer Science，Panzhihua University，Panzhihua 617000，China3.School of Information and Engineering,Sichuan Tourism University, Chengdu 610100, China4.School of Communication and Information Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China |
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| Abstract: | 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 weighted ?2,1 norm and MCP norm for the sparse features and low-rank constraints, and applying ?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. |
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