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首页> 外文期刊>Signal processing >Hyperspectral image denoising via global spatial-spectral total variation regularized nonconvex local low-rank tensor approximation
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Hyperspectral image denoising via global spatial-spectral total variation regularized nonconvex local low-rank tensor approximation

机译：高光谱图像去噪通过全局空间 - 光谱总变化正则化非凸显局部低级张量近似

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摘要

Hyperspectral image (HSI) denoising aims to restore clean HSI from the noise-contaminated one which is usually caused during data acquisition and conversion. In this paper, we propose a novel spatial-spectral total variation (SSTV) regularized nonconvex local low-rank (LR) tensor approximation method to remove mixed noise in HSIs. From one aspect, the clean HSI data have its underlying local LR tensor property, even though the real HSI data is not globally low-rank due to the non-independent and non-identically distributed noise and out-liers. According to this fact, we propose a novel tensor L_γ-norm to formulate the local LR prior. From another aspect, HSIs are assumed to be piecewisely smooth in the global spatial and spectral domains. Instead of traditional bandwise total variation, we use the SSTV regularization to simultaneously consider global spatial and spectral smoothness. Results on simulated and real HSI datasets indicate that the use of local LR tensor penalty and global SSTV can boost the preserving of local details and overall structural information in HSIs.
机译：高光谱图像（HSI）去噪旨在从噪声污染的噪声污染的HSI恢复通常在数据采集和转换期间引起的。在本文中，我们提出了一种新的空间光谱总变化（SSTV）正则化非耦合局部低秩（LR）张量近似方法，以去除HSIS中的混合噪声。从一个方面来看，即使真正的HSI数据由于非独立性和非相同的分布式噪声和超级噪声和超级噪声和超出层而不是全球低位，也具有其底层本地LR张量属性。根据这一事实，我们提出了一种新的张量L_γ-Norm，以在之前制定本地LR。从另一方面，假设HSIS在全局空间和光谱域中是分段光滑的。我们使用SSTV正则化而不是传统的乐队总变化，同时考虑全局空间和光谱平滑度。模拟和真实HSI数据集的结果表明，使用本地LR张力罚款和全球SSTV可以提高HSIS中的本地细节和整体结构信息。

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