Global Quasi-Minimal Residual Method for Image Restoration

The global quasi-minimal residual (QMR) method is a popular iterative method for the solution of linear systems with multiple right-hand sides. In this paper, we consider the application of the global QMR method to classical ill-posed problems arising from image restoration. Since the scale of the problem is usually very large, the computations with the blurring matrix can be very expensive. In this regard, we use a Kronecker product approximation of the blurring matrix to benefit the computation. In order to reduce the disturbance of noise to the solution, the Tikhonov regularization technique is adopted to produce better approximation of the desired solution. Numerical results show that the global QMR method outperforms the classic CGLS method and the global GMRES method.