Image restoration refers to the problem of removal or reduction of degradation in blurred noisy images. The image
degradation is usually modeled by a linear blur and an additive white noise process. The linear blur involved is always an ill-conditioned
which makes image restoration problem an ill-posed problem for which the solutions are unstable. Procedures adopted to stabilize the
inversion of ill-posed problem are called regularization, so the selection of regularization parameter is very important to the effect of
image restoration. In this paper, we study some numerical techniques for solving this ill-posed problem. Dynamical systems method
(DSM), Tikhonov regularization method, L-curve method and generalized cross validation (GCV) are presented for solving this illposed
problems. Some test examples and comparative study are presented. From the numerical results it is clear that DSM showed
improved restored images compared to L-curve and GCV.