Destriping is one of typical problems in remote sensing image processing which is crucial in subsequent applications. The orientation of the stripe in remote sensing image plays a key role in the destriping methods. However, the direction of the oblique stripe noises is uncertain which makes the destriping methods for vertical/horizontal stripes no longer applicable for the oblique ones. To address the issue, we propose an anisotropic total variation (TV) model for oblique stripe noise removal. The model is constructed by combining L1 norm regularization and anisotropic total variation regularization, where the L1 norm regularizations represent the global characteristics and orientation of stripe noise, anisotropic total variation regularizations describe the different effects of oblique stripe noise on the smoothness of the original clear image along the vertical and horizontal directions, which can preserve the strong edges and geometric features while suppressing stripes. In order to solve this model, we design an effective alternating direction method of multipliers (ADMM) algorithm with guaranteed convergence. The experimental results over simulated and real datasets demonstrate that the proposed approach can not only effectively remove oblique stripe noise, but also for vertical/horizontal ones, and outperform the related state-of-the-art methods.

去条纹是遥感图像处理中的典型问题之一，这在后续应用中至关重要。条纹在遥感图像中的方向在去条纹方法中起关键作用。然而，倾斜条纹噪声的方向是不确定的，这使得用于垂直/水平条纹的去条纹方法不再适用于倾斜条纹。为了解决这个问题，我们提出了一种各向异性总变化（TV）模型来去除斜条纹噪声。该模型是通过将L1范数正则化和各向异性总变化正则化相结合而构建的，其中L1范数正则化表示条带噪声的全局特征和方向，各向异性总变化正则化描述了斜条纹噪声对原始清晰图像沿垂直和水平方向的平滑度的不同影响，可以在抑制条纹的同时保留强烈的边缘和几何特征。为了解决该模型，我们设计了一种有效的乘积交替方向算法（ADMM），并保证了收敛性。在模拟数据集和真实数据集上的实验结果表明，该方法不仅可以有效地消除斜条纹噪声，而且还可以消除垂直/水平噪声，并且优于相关的最新技术。我们设计了一种具有保证收敛性的有效乘数交替方向法（ADMM）算法。在模拟数据集和真实数据集上的实验结果表明，该方法不仅可以有效地消除斜条纹噪声，而且还可以消除垂直/水平噪声，并且优于相关的最新技术。我们设计了一种具有保证收敛性的有效乘数交替方向法（ADMM）算法。在模拟数据集和真实数据集上的实验结果表明，该方法不仅可以有效地消除斜条纹噪声，而且还可以消除垂直/水平噪声，并且优于相关的最新技术。