Department of Mathematics and System, School of Sciences, National University of Defense Technology, Changsha, China.
IEEE Trans Image Process. 2012 Apr;21(4):1650-62. doi: 10.1109/TIP.2011.2172801. Epub 2011 Oct 19.
Multiplicative noise removal based on total variation (TV) regularization has been widely researched in image science. In this paper, inspired by the spatially adapted methods for denoising Gaussian noise, we develop a variational model, which combines the TV regularizer with local constraints. It is also related to a TV model with spatially adapted regularization parameters. The automated selection of the regularization parameters is based on the local statistical characteristics of some random variable. The corresponding subproblem can be efficiently solved by the augmented Lagrangian method. Numerical examples demonstrate that the proposed algorithm is able to preserve small image details, whereas the noise in the homogeneous regions is sufficiently removed. As a consequence, our method yields better denoised results than those of the current state-of-the-art methods with respect to the signal-to-noise-ratio values.
基于全变差（TV）正则化的乘法噪声去除在图像科学中得到了广泛的研究。在本文中，受适用于高斯噪声去噪的空间自适应方法的启发，我们开发了一种变分模型，它将 TV 正则化器与局部约束相结合。它也与具有空间自适应正则化参数的 TV 模型相关。正则化参数的自动选择基于一些随机变量的局部统计特征。相应的子问题可以通过增广拉格朗日方法有效地求解。数值实验表明，所提出的算法能够保留小的图像细节，而均匀区域的噪声则被充分去除。因此，与目前最先进的方法相比，我们的方法在信噪比值方面可以获得更好的去噪效果。
