Cascadic multilevel methods for fast nonsymmetric blur- and noise-removal

Image deblurring is a discrete ill-posed problem. This paper discusses cascadic multilevel methods designed for the restoration of images that have been contaminated by nonsymmetric blur and noise. Prolongation is carried out by nonlinear edge-preserving and noise-reducing operators, while restrictions are determined by weighted local least-squares approximation. The restoration problem is on each level solved by an iterative method, with the number of iterations determined by the discrepancy principle. The performance of several iterative methods is compared. Computed examples demonstrate the effectiveness of the image restoration methods proposed. The discrepancy principle requires that an estimate of the norm of the noise in the contaminated image be available. We illustrate how such an estimate can be computed with the aid of the nonlinear Perona–Malik diffusion equation.