Optimal space-varying regularization in iterative image restoration

Abstract

It has been shown that space-variant regularization in image restoration provides better results than space-invariant regularization. However, the optimal choice of the regularization parameter is usually unknown a priori. In previous work, the generalized cross-validation (GCV) criterion was shown to provide accurate estimates of the optimal regularization parameter. The author introduces a modified form of the GCV criterion that incorporates space-variant regularization and data error terms. Furthermore, he presents an efficient method for estimating the GCV criterion for the space-variant case using iterative image restoration techniques. This method performs nearly as well as the exact criterion for the image restoration problem. In addition, he proposes a Wiener filter interpretation for choosing the local weighting of the regularization. This interpretation suggests the use of a multistage estimation procedure to estimate the optimal choice of the local regularization weights. Experiments confirm the value of the modified GCV estimation criterion as well as the multistage procedure for estimating the local regularization weights