论文标题
高光谱图像通过全局空间 - 光谱总正规变异非凸局局部低级张量近似
Hyperspectral Image Denoising via Global Spatial-Spectral Total Variation Regularized Nonconvex Local Low-Rank Tensor Approximation
论文作者
论文摘要
高光谱图像（HSI）denoising旨在恢复受噪声污染的HSI的清洁HSI。噪声污染通常是在数据获取和转换期间引起的。在本文中，我们提出了一种新型的空间 - 谱系总变异（SSTV）正则非凸局局部低级别（LR）张量近似方法，以去除HSIS中的混合噪声。从一个方面来看，干净的HSI数据具有其基本的局部LR张量属性，即使由于liers和非高斯噪声，实际的HSI数据可能不会在全球范围内较低。根据这个事实，我们提出了一种新颖的张量$L_γ$ -Norm来制定本地LR先验。从另一个方面来看，HSI被认为在整体空间和光谱域中是单独平滑的。我们使用SSTV正则化而不是传统的带状变化，同时考虑相邻频段的全局空间结构和光谱相关性。对模拟和实际HSI数据集的结果表明，使用本地LR张量惩罚和全球SSTV可以提高HSIS中本地细节和整体结构信息的保存。
Hyperspectral image (HSI) denoising aims to restore clean HSI from the noise-contaminated one. Noise contamination can often be 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 may not be globally low-rank due to out-liers and non-Gaussian noise. 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 structure and spectral correlation of neighboring bands. 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.
