On selection of spatial-varying regularization parameters in total variation image restoration

In this paper, we consider and study total variation (TV) image restoration. The main aim of this paper is to develop a fast TV image restoration method with an automatic selection of spatial-varying regularization parameter scheme to restore blurred and noisy images. The method exploits the generalized cross-validation (GCV) technique to determine inexpensively how much regularization used in each region of an image and in each restoration step. By updating the regularization parameter in each iteration, the restored image can be obtained. Our experimental results show that the visual quality of restored images by the proposed method is very good even without prior knowledge of the original image. We will demonstrate the proposed method is also very efficient.