Affiliation:
1. Shaanxi Normal University
Abstract
Nonconvex nonsmooth potential functions have superior restoration performance for the images with neat boundaries. But the nondifferentiality could cause many numerical difficulties. Thus the graduated nonconvex (GNC) method is suggested to deal with these problems. In this paper, a class of nonconvex nonsmooth approximate potential functions have been constructed, which can help our get a better initial value of the original problem. The numerical results show the restored perfprmance of the proposed methods.
Publisher
Trans Tech Publications, Ltd.
Reference14 articles.
1. M. R. Banham and A. K. Katsaggelos, Digital image restoration, IEEE Trans. Signal Process., vol. 14, pp.24-41, (1997).
2. R. C. Gonzalez and R. E. Woods, Digital image processing, second ed, Prentice Hall, New Jersey, (2002).
3. X. X. Guo, F. Li, and M. K. Ng, A fast -TV algorithm for image restoration, SIAM J. Sci. Comput., vol. 31, pp.2322-2341, (2009).
4. H. Y. Fu, M. K. Ng, M. Nikolova, and J. L. Barlow, Efficient minimization methods of mixed and norms for image restoration, SIAM J. Sci. Comput., vol. 27, pp.1881-1902, (2006).
5. A. M. Bruckstein, D. L. Donoho and M. Elad, From sparse solutions of systems ofequationsto sparse modeling of signals and images, SIAM Rev., vol. 51, pp.34-81, (2009).
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献