中文摘要：

图象分割是在图象科学的一个热话题。在这份报纸我们在场一个新变化分割模型基于 Mumford-Shah 的理论当模特儿。我们的模型的目的是划分 noised 图象，根据某个标准，进应该对应于在兴趣的景色或目标的结构的单位的同类、光滑的区域。一个规则化术语，和不同忠实称为的建议基于区域的模型使用总数变化能在物理噪音的情况中被用于图象分割，例如 Gaussian，泊松和趋于增加的点缀噪音。我们的模型由五项加权的条款组成，他们中的二个负责图象基于忠实术语和全部的变化术语降噪，其它保证三坚持调节到数据，变光滑，并且断绝察觉马上被遇见。我们也为我们的模型开发一个最初双的混合坡度算法。各种各样的合成、真实的图象上的数字结果被提供把我们的方法与其它作比较，这些结果证明我们的建议模型和算法是有效的。

英文摘要：

Image segmentation is a hot topic in image science. In this paper we present a new variational segmentation model based on the theory of Mumford-Shah model. The aim of our model is to divide noised image, according to a certain criterion, into homogeneous and smooth regions that should correspond to structural units in the scene or objects of interest. The proposed region-based model uses total variation as a regularization term, and different fidelity term can be used for image segmentation in the cases of physical noise, such as Gaussian, Poisson and multiplicative speckle noise. Our model consists of five weighted terms, two of them are responsible for image denoising based on fidelity term and total variation term, the others assure that the three conditions of adherence to the data, smoothing, and discontinuity detection are met at once. We also develop a primal-dual hybrid gradient algorithm for our model. Numerical results on various synthetic and real images are provided to compare our method with others, these results show that our proposed model and algorithms are effective.