Natural images often have intrinsic low-rank structures and are susceptible to interference from outliers or perturbation noise, especially mixed noise. Low-rank matrix recovery via outlier estimation (ROUTE) has been proposed to determine the location of gross corruption by estimating the outliers; however, this approach ignores local structural smoothness. In this paper, we incorporate TV norm regularization into the ROUTE model of low-rank matrix recovery, which is called SSROUTE. This model can ensure structural smoothness in image denoising that is vulnerable to outlier noise and additive white Gaussian noise simultaneously. In addition, to solve the reformulated optimal problem, we develop an algorithm based on the alternating direction method of multipliers. Experimental results show that the proposed algorithm achieves a competitive denoising performance, especially for mixed noise.

自然图像通常具有内在的低秩结构，并且容易受到异常值或扰动噪声的干扰，尤其是混合噪声。已经提出了通过异常值估计（ROUTE）的低秩矩阵恢复，以通过估计异常值来确定严重腐败的位置；然而，这种方法忽略了局部结构的平滑性。在本文中，我们将 TV 范数正则化纳入低秩矩阵恢复的 ROUTE 模型，称为 SSROUTE。该模型可以确保图像去噪时的结构平滑，同时容易受到异常噪声和加性高斯白噪声的影响。此外，为了解决重新制定的最优问题，我们开发了一种基于乘法器交替方向法的算法。