Restoration theory for digital images under additive noises by a constrained generalized inverse matrix

The image restoration by using Moore-Penrose's generalized inverse (MPGI) is quite simple to implement, but it has vital defects: (a) it must compute the eigenvalues and eigenvectors of a large scale matrix, (b) restored images by this method are badly affected by the additive noise. In this paper, a new image restoration by the MPGI constrained by a quality measure of restored images is proposed, which can remove the defects of the current MPGI-restoration. Prior information the proposed image restoration requires is only a matrix which represents the point-spread function of degradation.