Department of Mathematics
A fast minimization method for blur and multiplicative noise removal
Multiplicative noise and blur removal problems have attracted much attention in recent years. In this paper, we propose an efficient minimization method to recover images from input blurred and multiplicative noisy images. In the proposed algorithm, we make use of the logarithm to transform blurring and multiplicative noise problems into additive image degradation problems, and then employ l 1-norm to measure in the data-fitting term and the total variation to measure the regularization term. The alternating direction method of multipliers (ADMM) is used to solve the corresponding minimization problem. In order to guarantee the convergence of the ADMM algorithm, we approximate the associated nonconvex domain of the minimization problem by a convex domain. Experimental results are given to demonstrate that the proposed algorithm performs better than the other existing methods in terms of speed and peak signal noise ratio. © 2013 Copyright Taylor and Francis Group, LLC.
Blur, image restoration, Iterative method, minimization, Multiplicative noise
Source Publication Title
International Journal of Computer Mathematics
Taylor & Francis
Link to Publisher's Edition
Wang, Fan, and Michael K. Ng. "A fast minimization method for blur and multiplicative noise removal." International Journal of Computer Mathematics 90.1 (2013): 48-61.