Rank-1 Tensor Decomposition for Hyperspectral Image Denoising with Nonlocal Low-Rank Regularization
Rank-1 Tensor Decomposition for Hyperspectral Image Denoising with Nonlocal Low-Rank Regularization
复制标题
DOI：
10.1109/cmvit.2017.22
复制
发表时间：
2017-02
期刊：
影响因子：
--
通讯作者：
Jize Xue;Yongqiang Zhao
中科院分区：
文献类型：
--
作者：
Jize Xue;Yongqiang Zhao
在高光谱图像去噪中，一阶张量分解（R1TD）模型可以联合利用空间和光谱信息，有效降低噪声。准确估计高光谱图像的秩是很困难的，并且秩的不确定性会导致R1TD去噪算法效率低下。非局部相似补丁的秩比图像低，它可以用于秩1张量分解过程，而不是显式估计秩参数。在这项工作中，引入非局部低秩正则化以避免秩不确定性影响去噪性能。然后设计了一种交替方向乘子法（ADMM）优化技术来解决最小化问题。与现有技术方法相比，所提出的算法在目视检查和图像质量指数方面显着提高了高光谱图像质量。
In hyperspectral imagery denoising, rank-1 tensor decomposition (R1TD) model can utilize the spatial and spectral information jointly and reduce the noise efficiently. It is difficult to estimate the rank of hyperspectral imagery accurately, and the rank uncertainty will make the R1TD denoising algorithm inefficient. The nonlocal similar patches have lower rank than image, it can be used in rank-1 tensor decomposition process instead of explicitly estimating rank parameters. In this work, a nonlocal low-rank regularization is introduced to avoid the rank uncertainty to influence denoising performance. Then an alternating direction method of multipliers (ADMM) optimization technique is designed to solve the minimum problem. Compared with the state of art methods, proposed algorithm significantly improves the hyperspectral imagery quality both in visual inspection and image quality indices.