Adaptive regularization for image restoration using a variational inequality approach

Authors

Matthew Kitchener, University of WollongongFollow
Abdesselam Bouzerdoum, University of WollongongFollow
Son Lam Phung, University of WollongongFollow

RIS ID

36484

Publication Details

M. Kitchener, A. Bouzerdoum & S. Phung, "Adaptive regularization for image restoration using a variational inequality approach," in ICIP 2010: IEEE 17th International Conference on Image Processing, 2010, pp. 2513-2516.

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.