纹理图像的分割是图像处理领域中的一个典型难题。不同于传统的提取纹理特征量进行纹理分割的方法，本文将图像复原和重建中的总变分最小化方法和活动围道分割方法相结合，提出了一种简单的线性纹理模型。利用总变分最小化方法在保持图像大尺度棱边信息的基础上对纹理体现的局部小尺度周期性灰度振动细节进行平滑得到简化的图像原型。对其进行分割获得不同纹理区域之间的低定位精度的边界围道，再利用原始图像对围道进行高精度细化。在总变分最小化导致的非线性扩散方程求解过程中，运用AOS(additive operatorr splitting)数值算法以改进算法效率。实验结果表明，该方法能很快提取出纹理图像的简化图像，同时是一种无监督的纹理分割方法。

This paper is devoted itself to segmentation of texture images. Based on the theory of total variation minimization and the active contour image segmentation method, we proposed a simple linear model of texture images which regards a texture image as a sum of a photo prototype image and a texture sub-image. Using the total variation minimization method the simplified prototype image can be extracted from the origin image. A coarse border can be located by segmenting this simplified image. Based on the coarse border, a higher accuracy result can be obtained by taking the original image into account. We choose the geometric MDL active contour for image segmentation and applied AOS scheme for the numerical solution of the nonlinear diffusion equation of the total variation minimization. Our method is unsupervised. Experiments on both synthetic and natural texture images show that the method is effective.