An effective algorithm for mean curvature-based image deblurring problem

The mean curvature-based image deblurring model is widely used in image restoration to preserve edges and remove staircase effect in the resulting images. However, the Euler–Lagrange equations of the mean curvature model lead to the challenging problem of solving a nonlinear fourth order integro-differential equation. Furthermore the discretization of the Euler–Lagrange equations produces a nonlinear ill-conditioned system which affects the convergence of the numerical algorithms such as Krylov subspace methods (GMRES, etc.) In this paper, we have treated the high order nonlinearity by converting the nonlinear fourth order integro-differential equation into a system of first order equations. To speed up convergence by GMRES method, we have introduced a new circulant preconditioned matrix. Fast convergence is assured by the proved analytical property of our proposed new preconditioner. The first order error estimates are also established for the finite difference discretization. The effectiveness of our algorithm can be observed through fast convergence rates in numerical examples.