A Generalized DAMRF Image Modeling for Superresolution of License Plates

In this paper, we propose a novel superresolution (SR) reconstruction algorithm to handle license plate texts in real traffic videos. To make license plate numbers more legible, a generalized discontinuity-adaptive Markov random field (DAMRF) model is proposed based on the recently reported bilateral filtering, which not only preserves edges but is robust to noise as well. Moreover, instead of looking for a fixed value for the regularization parameter, a method for automatically estimating it is applied to the proposed model based on the input images. Information needed to determine the regularization parameter is updated at each iteration step, which is based on the available reconstructed image. Finally, we use the graduated nonconvexity optimization procedure to minimize the cost function. Results on synthetic and real traffic sequences are presented, which show the effectiveness of the proposed method and demonstrate its superiority to the conventional DAMRF SR method.