Impulse Noise Image Restoration Using Nonconvex Variational Model and Difference of Convex Functions Algorithm
Impulse Noise Image Restoration Using Nonconvex Variational Model and Difference of Convex Functions Algorithm
复制标题
非凸变分模型和凸函数差分算法的脉冲噪声图像恢复
DOI：
10.1109/tcyb.2022.3225525
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发表时间：
2022-12
影响因子：
11.8
通讯作者：
Kwong Sam
中科院分区：
文献类型：
--
作者：
Zhang Benxin;Zhu Guopu;Zhu Zhibin;Zhang Hongli;Zhou Yicong;Kwong Sam
本文研究了脉冲噪声图像的恢复问题。消除脉冲噪声的一种典型方法是使用$L_{1}$范数数据拟合项和总变分（TV）正则化。然而，以这种方式设计的凸优化方法总是会产生阶梯伪影。此外，$L_{1}$范数拟合项倾向于对损坏和无噪声的数据进行同等惩罚，并且对脉冲噪声不具有鲁棒性。为了寻求高恢复质量的解，我们提出了一种将非凸数据拟合项和非凸电视正则化相结合的变分模型。非凸电视正则化器的使用有助于消除楼梯伪影。此外，非凸保真度项可以有效地检测脉冲噪声，当观察到的数据轻微损坏时，它被强制执行，而对于严重损坏的像素，它被强制执行的程度较低。本文还提出了一种新的凸函数差分算法来求解变分模型。利用变分方法证明了该算法生成的序列收敛于非凸目标函数的一个平稳点。实验结果表明，本文提出的算法是有效的，并与现有的方法相比较。
In this article, the problem of impulse noise image restoration is investigated. A typical way to eliminate impulse noise is to use an $L_{1}$ norm data fitting term and a total variation (TV) regularization. However, a convex optimization method designed in this way always yields staircase artifacts. In addition, the $L_{1}$ norm fitting term tends to penalize corrupted and noise-free data equally, and is not robust to impulse noise. In order to seek a solution of high recovery quality, we propose a new variational model that integrates the nonconvex data fitting term and the nonconvex TV regularization. The usage of the nonconvex TV regularizer helps to eliminate the staircase artifacts. Moreover, the nonconvex fidelity term can detect impulse noise effectively in the way that it is enforced when the observed data is slightly corrupted, while is less enforced for the severely corrupted pixels. A novel difference of convex functions algorithm is also developed to solve the variational model. Using the variational method, we prove that the sequence generated by the proposed algorithm converges to a stationary point of the nonconvex objective function. Experimental results show that our proposed algorithm is efficient and compares favorably with state-of-the-art methods.
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DOI：
10.1137/10080172x
发表时间：
2013-01
期刊：
SIAM J. Sci. Comput.
影响因子：
--
作者：
M. Nikolova;M. Ng;Chi-Pan Tam
通讯作者：
M. Nikolova;M. Ng;Chi-Pan Tam
影响因子：
4.5
作者：
Zhang, Cun-Hui
通讯作者：
Zhang, Cun-Hui
影响因子：
5.4
作者：
Selesnick, Ivan;Farshchian, Masoud
通讯作者：
Farshchian, Masoud
影响因子：
4
作者：
RUDIN, LI;OSHER, S;FATEMI, E
通讯作者：
FATEMI, E
使用广义非凸正则化有效稳健地恢复稀疏信号和图像
影响因子：
5.4
作者：
Wen, Fei;Pei, Ling;Liu, Peilin
通讯作者：
Liu, Peilin