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Hyperspectral Image Denoising via Global Spatial-Spectral Total Variation Regularized Nonconvex Local Low-Rank Tensor Approximation
Signal Processing ( IF 4.4 ) Pub Date : 2021-01-01 , DOI: 10.1016/j.sigpro.2020.107805
Haijin Zeng , Xiaozhen Xie , Jifeng Ning

Abstract 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 Lγ-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.

中文翻译:

通过全局空间光谱总变异正则化非凸局部低阶张量近似的高光谱图像去噪

摘要 高光谱图像(HSI)去噪旨在从通常在数据采集和转换过程中引起的噪声污染中恢复干净的HSI。在本文中,我们提出了一种新的空间谱总变分 (SSTV) 正则化非凸局部低秩 (LR) 张量近似方法来去除 HSI 中的混合噪声。从一方面来看,干净的 HSI 数据具有其潜在的局部 LR 张量特性,尽管由于非独立和非相同分布的噪声和异常值,真实的 HSI 数据不是全局低秩的。根据这一事实,我们提出了一种新的张量 Lγ 范数来制定局部 LR 先验。另一方面,假设 HSI 在全局空间和光谱域中是分段平滑的。代替传统的带状全变,我们使用 SSTV 正则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,使用局部 LR 张量惩罚和全局 SSTV 可以促进 HSI 中局部细节和整体结构信息的保留。
更新日期:2021-01-01
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