Multiframe Superresolution of Vehicle License Plates Based on Distribution Estimation Approach

Abstract

Low-resolution (LR) license plate images or videos are often captured in the practical applications. In this paper, a distribution estimation based superresolution (SR) algorithm is proposed to reconstruct the license plate image. Different from the previous work, here, the high-resolution (HR) image is estimated via the obtained posterior probability distribution by using the variational Bayesian framework. To regularize the estimated HR image, a feature-specific prior model is proposed by considering the most significant characteristic of license plate images; that is, the target has high contrast with the background. In order to assure the success of the SR reconstruction, the models representing smoothness constraints on images are also used to regularize the estimated HR image with the proposed feature-specific prior model. We show by way of experiments, under challenging blur with size 7 × 7 and zero-mean Gaussian white noise with variances 0.2 and 0.5, respectively, that the proposed method could achieve the peak signal-to-noise ratio (PSNR) of 22.69 dB and the structural similarity (SSIM) of 0.9022 under the noise with variance 0.2 and the PSNR of 19.89 dB and the SSIM of 0.8582 even under the noise with variance 0.5, which are 1.84 dB and 0.04 improvements in comparison with other methods.