The hyperspectral-image (HSI) noise-reduction step is a very significant preprocessing phase of data-quality enhancement. It has been attracting immense research attention in the remote sensing and image processing domains. Many methods have been developed for HSI restoration, the goal of which is to remove noise from the whole HSI cube simultaneously without considering the spectral–spatial similarity. When a noise-removal algorithm is used globally to the entire data set, it would not eliminate all levels of noise, effectively. Furthermore, most of the existing methods remove independent and identically distributed (i.i.d.) Gaussian noise. The real scenarios are much more complicated than this assumption. The complexity created by natural noise that has a non-i.i.d. structure leads to inefficient methods containing underestimation and invalid performance. In this article, we calculated the spatial–spectral similarity criteria by defining a set of clustering-based latent variables (CLVs) in a Bayesian framework to improve the robustness. These criteria can be extracted using the clustering operators. Then, by applying the CLV to the variational Bayesian model, we investigated a new low-rank matrix factorization denoising approach based on the proposed clustering-based latent variable (CLV-LRMF) to remove noise with the non-i.i.d. mixture of Gaussian structures. Finally, we switched to the GPU for MATLAB implementation to reduce the runtime. The experimental results show that the performance has been improved by applying the proposed CLV and demonstrate the effectiveness of the proposed CLV-LRMF over other state-of-the-art methods.

中文翻译：

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贝叶斯框架中基于聚类的潜在变量的高光谱图像降噪

高光谱图像（HSI）降噪步骤是数据质量增强的非常重要的预处理阶段。它已经在遥感和图像处理领域引起了巨大的研究关注。已经开发出许多用于HSI恢复的方法，其目的是在不考虑频谱空间相似性的情况下同时从整个HSI立方体中消除噪声。当对整个数据集全局使用噪声消除算法时，它无法有效消除所有级别的噪声。此外，大多数现有方法都消除了独立且分布均匀的（iid）高斯噪声。实际场景比这个假设要复杂得多。具有非iid的自然噪声所产生的复杂性 结构导致方法效率低下，其中包含低估和无效性能。在本文中，我们通过在贝叶斯框架中定义一组基于聚类的潜在变量（CLV）来计算空间光谱相似性标准，以提高鲁棒性。可以使用聚类运算符提取这些条件。然后，通过将CLV应用于变分贝叶斯模型，我们研究了一种新的低秩矩阵分解降噪方法，该方法基于拟议的基于聚类的潜在变量（CLV-LRMF）去除高斯结构的非iid混合噪声。最后，我们切换到GPU进行MATLAB实现，以减少运行时间。