Hyperspectral image (HSI) is easily corrupted by different kinds of noise, such as stripes, dead pixels, impulse noise, Gaussian noise, etc. Due to less consideration of the structural specificity of stripes, many existing HSI denoising methods cannot effectively remove the heavy stripes in mixed noise. In this paper, we classify the noise on HSI into three types: sparse noise, stripe noise, and Gaussian noise. The clean image and different types of noise are treated as independent components. In this way, the image denoising task can be naturally regarded as an image decomposition problem. Thanks to the structural characteristic of stripes and the low-rank property of HSI, we propose to destripe and denoise the HSI by using stripe and spectral low-rank matrix recovery and combine it with the global spatial-spectral TV regularization (SSLR-SSTV). By considering different properties of different HSI ingredients, the proposed method separates the original image from the noise components perfectly. Both simulation and real image denoising experiments demonstrate that the proposed method can achieve a satisfactory denoising result compared with the state-of-the-art methods. Especially, it outperforms the other methods in the task of stripe noise removal visually and quantitatively.

中文翻译：

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使用条纹和光谱低秩矩阵恢复以及全局空间光谱总变化对高光谱图像进行去条纹和去噪

高光谱图像（HSI）容易被不同类型的噪声破坏，例如条纹，坏点，脉冲噪声，高斯噪声等。由于对条纹的结构特异性的考虑较少，许多现有的HSI降噪方法无法有效去除繁琐的图像。混合噪声中出现条纹。在本文中，我们将HSI上的噪声分为三种类型：稀疏噪声，条带噪声和高斯噪声。干净的图像和不同类型的噪声被视为独立的组件。这样，图像去噪任务自然可以被视为图像分解问题。由于条纹的结构特征和HSI的低秩特性，我们建议通过使用条纹和频谱低秩矩阵恢复来对HSI进行去条纹和去噪，并将其与全局空间光谱电视正则化（SSLR-SSTV）结合使用。通过考虑不同恒指成分的不同特性，该方法将原始图像与噪声成分完美分离。仿真和实像去噪实验均表明，与现有方法相比，该方法可以达到满意的去噪效果。特别是，它在视觉和定量上消除条带噪声的任务上都优于其他方法。