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Hyperspectral Image Denoising via Subspace Low-rank Representation and Spatial-spectral Total Variation

机译：通过子空间低秩表示和空间光谱总变化进行高光谱图像降噪

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Hyperspectral images (HSIs) acquired actually often contain various types of noise, such as Gaussian noise, impulse noise, and dead lines. On the basis of land covers, the spectral vectors in HSI can be separated into different classifications, which means the spectral space can be regarded as a union of several low-rank (LR) subspaces rather than a single LR subspace. Recently, LR constraint has been widely applied for denoising HSI. However, those LR-based methods do not constrain the intrinsic structure of spectral space. And these methods cannot make better use of the spatial or spectral features in an HSI cube. In this article, a framework named subspace low-rank representation combined with spatial-spectral total variation regularization (SLRR-SSTV) is proposed for HSI denoising, where the SLRR is introduced to more precisely satisfy the low-rank property of spectral space, and the SSTV regularization is involved for the spatial and spectral smoothness enhancement. An inexact augmented Lagrange multiplier method by alternative iteration is employed for the SLRR-SSTV model solution. Both simulated and real HSI experiment results demonstrate that the proposed method can achieve a state-of-the-art performance in HSI denoising. (C) 2020 Society for Imaging Science and Technology.
机译：实际上，获取的高光谱图像（HSI）通常包含各种类型的噪声，例如高斯噪声，脉冲噪声和虚线。基于土地覆盖，可以将HSI中的频谱矢量分为不同的类别，这意味着频谱空间可以视为几个低秩（LR）子空间的并集，而不是单个LR子空间的并集。近来，LR约束已被广泛地用于对HSI进行降噪。但是，那些基于LR的方法并不限制光谱空间的固有结构。而且这些方法无法更好地利用HSI立方体中的空间或光谱特征。在本文中，提出了一种用于子空间低秩表示并结合空间频谱总变化正则化（SLRR-SSTV）的HSI去噪框架，其中引入SLRR以更精确地满足频谱空间的低秩性质，并且SSTV正则化涉及空间和频谱平滑度的增强。 SLRR-SSTV模型解决方案采用交替迭代的不精确增强Lagrange乘法器方法。模拟和真实HSI实验结果均表明，该方法可以实现HSI去噪的最新技术。 （C）2020年成像科学与技术学会。

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    《Journal of Imaging Science and Technology》 |2020年第1期|010507.1-010507.9|共9页
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    Nanjing Univ Posts & Telecommun Sch Sci 9 Wenyuan Rd Nanjing 210023 Peoples R China;

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