Hyperspectral images (HSIs) can finely discriminate distinct objects with a high spectral resolution, and they are widely employed in various applications. However, mixed noise severely degrades the quality of HSI and restricts the performance of subsequent tasks. As one of the critical preprocessing steps, HSI denoising has been developed rapidly, among which low-rank (LR) prior-based methods have achieved superior performance. Nevertheless, the existing approaches frequently fail to completely remove noise and reconstruct high-quality HSIs when tackling complicated mixed noise with multiple types of high-intensity stripe noise. To solve this problem, we propose an HSI denoising and destriping method based on anisotropic spatial and spectral total variation regularized double LR approximation (ATVDLR). The double LR (DLR) approximation framework is devoted to separating the clean image from the mixed noise by exploiting both the global correlations of the HSI tensor and the LR structure of stripe noise. Furthermore, the anisotropic spatial and spectral total variation (ASSTV) regularization is introduced to preserve the spatial–spectral smoothness of HSI and the directional feature of stripes, thereby further suppressing high-level stripes and Gaussian noise. Finally, the alternating direction method of multipliers (ADMMs) technique is designed to solve the proposed ATVDLR model. Extensive experimental results indicate that the proposed method outperforms other state-of-the-art techniques in multitype high-intensity mixed noise reduction and image structural information protection, and has superior performance in complex mixed noise removal of real Gaofen-5 (GF-5) HSIs.

中文翻译：

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HSI去噪去条纹的各向异性空间-谱全变分正则化双低阶近似


高光谱图像（HSI）可以以高光谱分辨率精细地区分不同的物体，并且广泛应用于各种应用中。然而，混合噪声严重降低了 HSI 的质量并限制了后续任务的性能。作为关键的预处理步骤之一，HSI去噪得到了迅速发展，其中基于低秩（LR）先验的方法取得了优越的性能。然而，在处理具有多种类型的高强度条纹噪声的复杂混合噪声时，现有方法常常无法完全去除噪声并重建高质量的HSI。为了解决这个问题，我们提出了一种基于各向异性空间和光谱全变分正则化双LR近似（ATVDLR）的HSI去噪和去条纹方法。双 LR (DLR) 近似框架致力于通过利用 HSI 张量的全局相关性和条纹噪声的 LR 结构来将干净图像与混合噪声分离。此外，引入各向异性空间和光谱全变分（ASSTV）正则化来保留HSI的空间光谱平滑性和条纹的方向特征，从而进一步抑制高水平条纹和高斯噪声。最后，设计了交替方向乘子法（ADMM）技术来求解所提出的 ATVDLR 模型。大量的实验结果表明，该方法在多种类型高强度混合噪声抑制和图像结构信息保护方面优于其他最先进的技术，并且在真实高芬-5（GF-5）复杂混合噪声去除方面具有优越的性能。 ) 恒指。