Vehicular Image Restoration Based on Variational Dirichlet Blur Kernel Estimation

When the vehicular image has noise, the estimated blur kernel is not accurate. Therefore a more accurate method based on Variational Dirichlet distribution is proposed to estimate blur kernel, combined with improved augmented Lagrangian to achieve effective image restoration. This method uses the gradient projection method to solve optimization problems and extract precise orientation of the image edge. The Dirichlet distribution substitutes posterior estimate to eliminate image noise and reduce the additional constraint. Hyper-Laplacian prior distribution modeling, together with ALM, is used to restore the vehicular blind image. Experiment results show that multi-scale blur kernel estimator can effectively estimate a blur kernel and eliminate noise of vehicular image, and the texture detail of vehicular image can also be recovered. Compared with other methods, the proposed blind image restoration method have better visual appearances and quality measurements.