A Multilevel Algorithm for Simultaneously Denoising and Deblurring Images

In this paper, we develop a fast multilevel algorithm for simultaneously denoising and deblurring images under the total-variation regularization. Although much effort has been devoted to developing fast algorithms for the numerical solution and the denoising problem was satisfactorily solved, fast algorithms for the combined denoising and deblurring model remain to be a challenge. Recently several successful studies of approximating this model and subsequently finding fast algorithms were conducted which have partially solved this problem. The aim of this paper is to generalize a fast multilevel denoising method to solving the minimization model for simultaneously denoising and deblurring. Our new idea is to overcome the complexity issue by a detailed study of the structured matrices that are associated with the blurring operator. A fast algorithm can then be obtained for directly solving the variational model. Supporting numerical experiments on gray scale images are presented. AMS classifications. 68U10, 65F10, 65K10.