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论文类型：期刊论文

发表时间：2012-01-01

发表刊物：INTERNATIONAL JOURNAL OF COMPUTER MATHEMATICS

收录刊物：SCIE

卷号：89

期号：2

页面范围：160-189

ISSN号：0020-7160

关键字：variational problems; optimization; Euler-Lagrange equation; 3D image segmentation; multi-grid algorithm; adaptive smoothers; local Fourier analysis

摘要：Variational segmentation models provide effective tools for image processing applications. Although existing models are continually refined to increase their capabilities, solution of such models is often a slow process, since fast methods are not immediately applicable to nonlinear problems. This paper presents an efficient multi-grid algorithm for solving the Chan-Vese model in three dimensions, generalizing our previous work on the topic in two dimensions, but this direct generalized method is low performance or unfeasible. So here, we first present two general smoothers for a nonlinear multi-grid method and then give our three new adaptive smoothers which can choose optimal a parameter of the smoothers automatically, also we analyse them using a local Fourier analysis and our theorem to inform how to obtain an optimal parameter and the best smoother selection. Finally, various advantages of our recommended algorithm are illustrated, using both synthetic and real images.