A variational formulation for physical noised image segmentation

  • Qiong Lou
  • Jia-lin Peng
  • De-xing KongEmail author


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.


image segmentation variational method image denoising primal-dual hybrid gradient algorithm non-Gaussian noise 

MR Subject Classification

65K10 68U10 49M30 


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Copyright information

© Editorial Committee of Applied Mathematics-A Journal of Chinese Universities and Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.Center of Mathematical SciencesZhejiang UniversityHangzhouChina
  2. 2.The School of Computer Science and TechnologyHuaqiao UniversityXiamenChina
  3. 3.Department of MathematicsZhejiang UniversityHangzhouChina

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