Nonlocal low-rank regularized two-phase approach for mixed noise removal
Nonlocal low-rank regularized two-phase approach for mixed noise removal
复制标题
用于混合噪声去除的非局部低秩正则化两阶段方法
DOI：
10.1088/1361-6420/ac0c21
复制
发表时间：
2021
期刊：
影响因子：
2.1
通讯作者：
Jian Lu
中科院分区：
文献类型：
--
作者：
Chen Xu;Xiaoxia Liu;Jian Zheng;Lixin Shen;Qingtang Jiang;Jian Lu
去除图像中的混合噪声是最近许多论文中讨论的一个难题。在本文中，我们解决了混合加性高斯白噪声和脉冲噪声的问题。我们建议通过非局部低秩正则化两阶段方法来消除这种混合噪声。在第一阶段，我们识别并标记可能被脉冲噪声损坏的像素。在第二阶段，通过未标记的观测数据恢复图像。恢复的图像是通过解决优化问题来实现的，该优化问题的目标函数具有 ℓ 1/ℓ 2 组合的内容相关保真度项和非凸非局部低秩正则化项。这两个术语都建立在由相似补丁形成的补丁矩阵之上。根据图像的先验知识，每个块矩阵被认为是低秩的。我们通过迭代自适应核范数最小化算法解决了这种非凸优化问题，并提供了其收敛性分析。我们的实验表明，所提出的方法在三个定量指标方面优于现有的最先进算法，即峰值信噪比、结构相似性和特征相似性以及恢复图像的视觉质量。
Removing mixed noise in images is a difficult problem which has been discussed in many recent papers. In this paper, we tackle the problem of having mixed additive Gaussian white noise and impulse noise. We propose to remove this mixed noise through a nonlocal low-rank regularized two-phase approach. In the first phase, we identify and label the pixels that are likely to be corrupted by the impulse noise. In the second phase, the image is restored through the unlabeled observed data. The restored image is achieved via solving an optimization problem whose objective function has an ℓ 1/ℓ 2 combined content-dependent fidelity term and a nonconvex nonlocal low-rank regularization term. Both terms are built on patch matrices formed from similar patches. Each patch matrix is considered to be low-rank according to the prior knowledge of images. We solve this nonconvex optimization through an iterative adaptive nuclear norm minimization algorithm and provide its convergence analysis. Our experiments show the proposed method outperforms the existing state-of-the-art algorithms in terms of three quantitative metrics, namely, the peak signal-to-noise ratio, the structural similarity and the feature similarity, and visual quality of the restored images.