基于全散度的变分CV模型及其分割算法

Variational CV Model Based on Total Bregman Divergence and its Segmentation Algorithm

CV模型在图像灰度不均匀或有噪声干扰时,易出现错分现象,因此将全散度引入变分CV模型,提出了基于全散度的变分CV模型及其迭代分割算法。分析基于欧氏距离所对应的变分CV模型分割算法存在的问题和不足,通过图示说明全散度相对于欧氏距离在距离计算与坐标系选择无关的优势,将其引入变分CV模型拟合偏差项,来提高图像灰度值与分割区域平均灰度偏差计算的鲁棒性。然后,采用欧拉-拉格朗日变分法获得全散度变分CV模型的偏微分方程,并采用数值计算方法获得该偏微分方程的迭代求解算法。同时在全散度变分CV模型中,增大拟合偏差项的权重系数,加大拟合偏差项在变分模型中的重要性。实验结果表明,全散度变分CV模型具有初始化敏感低、抗噪性强、鲁棒性高等优点。

The classical CV model is not completely suitable to segment the gray image which is intensity inhomogeneity,and has been disturbed by Gaussian noises with some variance.The variational CV model based on the total Bregman divergence was proposed and its iterative segmentation algorithm was presented.Firstly,the problems and disadvantages of the variational CV model segmentation method constructed by the Euclidean metric are analyzed.Secondly,compared with Euclidean metric,a figure shows the advantages of the total Bregman divergence that there is no connection with coordinate system in the distance calculation.Then,to reach the purpose of reducing noise sensitivity and enhance robustness of image segmentation,the data deviation term in CV model is built by the total Bregman divergence.Finally,Euler-Lagrange equation of this proposed variational model is obtained by variational method,and the variational model algorithm of the image segmentation is presented by numerical computation method.In addition,to accelerate the convergence rate,the weighting parameters of fitting terms should appropriately chose bigger value,and the importance of fitting items increases in variational model.The experimental results show that the proposed method is low sensitive to initialize contour curve,and has good anti-noise and robust performance.