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Adaptive regularization for image restoration using a variational inequality approach

Conference Paper


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Abstract


  • In this paper, a generalized image restoration method is formulated as a variational inequality problem, whose solution is obtained using a dynamic system approach. In this method, the restored image and the regularization parameter are obtained simultaneously. In particular, the optimum regularization parameter is determined adaptively, depending on noise and image content. The restoration problem is presented in a generalized form so that it maybe be implemented using different norms; only L1 and L2 norms have been implemented in this paper. A comparison based on experimental results shows that the proposed method achieves comparable if not better performance as some of the existing state-of-the-art techniques.

Publication Date


  • 2010

Citation


  • M. Kitchener, A. Bouzerdoum & S. Phung, "Adaptive regularization for image restoration using a variational inequality approach," in International Conference on Image Processing, 2010, pp. 2513-2516.

Scopus Eid


  • 2-s2.0-78651110826

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=1848&context=infopapers

Ro Metadata Url


  • http://ro.uow.edu.au/infopapers/835

Has Global Citation Frequency


Start Page


  • 2513

End Page


  • 2516

Place Of Publication


  • USA

Abstract


  • In this paper, a generalized image restoration method is formulated as a variational inequality problem, whose solution is obtained using a dynamic system approach. In this method, the restored image and the regularization parameter are obtained simultaneously. In particular, the optimum regularization parameter is determined adaptively, depending on noise and image content. The restoration problem is presented in a generalized form so that it maybe be implemented using different norms; only L1 and L2 norms have been implemented in this paper. A comparison based on experimental results shows that the proposed method achieves comparable if not better performance as some of the existing state-of-the-art techniques.

Publication Date


  • 2010

Citation


  • M. Kitchener, A. Bouzerdoum & S. Phung, "Adaptive regularization for image restoration using a variational inequality approach," in International Conference on Image Processing, 2010, pp. 2513-2516.

Scopus Eid


  • 2-s2.0-78651110826

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=1848&context=infopapers

Ro Metadata Url


  • http://ro.uow.edu.au/infopapers/835

Has Global Citation Frequency


Start Page


  • 2513

End Page


  • 2516

Place Of Publication


  • USA