Zhang Benxin, Wang Xiaolong, Li Yi, Zhu Zhibin
School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin 541004, China.
School of Mathematics and Computing Science, Guilin University of Electronic Technology, Guilin 541004, China.
Math Biosci Eng. 2023 Jul 7;20(8):14777-14792. doi: 10.3934/mbe.2023661.
Total variation (TV) regularizer has diffusely emerged in image processing. In this paper, we propose a new nonconvex total variation regularization method based on the generalized Fischer-Burmeister function for image restoration. Since our model is nonconvex and nonsmooth, the specific difference of convex algorithms (DCA) are presented, in which the subproblem can be minimized by the alternating direction method of multipliers (ADMM). The algorithms have a low computational complexity in each iteration. Experiment results including image denoising and magnetic resonance imaging demonstrate that the proposed models produce more preferable results compared with state-of-the-art methods.
总变分（TV）正则化器已在图像处理中广泛出现。在本文中，我们提出了一种基于广义费舍尔 - 伯梅斯特函数的新型非凸总变分正则化方法用于图像恢复。由于我们的模型是非凸且非光滑的，我们给出了凸算法的特定差异（DCA），其中子问题可通过乘子交替方向法（ADMM）最小化。该算法在每次迭代中具有较低的计算复杂度。包括图像去噪和磁共振成像在内的实验结果表明，与现有方法相比，所提出的模型产生了更优的结果。
