A hybrid optimization method for multiplicative noise and blur removal

The main contribution of this paper is to propose a new hybrid optimization method for the multiplicative noise and blur removal problem. A degraded image can often be recovered efficiently by minimizing an objective function which consists of a data-fidelity term and a regularization term. In the paper, we apply the quadratic penalty function method combined with the alternating direction method to minimize the corresponding objective function. Numerical experiments are presented to demonstrate the effectiveness of the proposed method. Experimental results illustrate the state-of-the-art performance of the proposed method to handle multiplicative noise and blur removal problem.