Super Resolution of License Plates Using Generalized DAMRF Image Modeling

Vikas Nivrutti Dhakane, Jalinder Nivrutti Ekatpure

Abstract: LPR (License Plate Recognition) is a main component of modern transportation management systems. It uses a set of computer image-processing technologies to identify vehicles by its license plate. We propose a novel super resolution (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. Character recognition is the core of LPR.

Keywords: Bilateral filter, Markov Random Field MRF, Maximum A Posteriori MAP, regularization, Super Resolution SR