$L_p$ -Norm-Based Sparse Regularization Model for License Plate Deblurring

作者：Chenping Zhao, Yingjun Wang, Hongwei Jiao, Jingben Yin, Xuezhi Li · 发表于：IEEE Access · 年份：2020 · DOI：10.1109/access.2020.2969675 · 被引用次数：8 · 研究领域：Vehicle License Plate Recognition、Image and Object Detection Techniques、Infrastructure Maintenance and Monitoring

We propose an$L_{p}$-norm-based sparse regularization model for license plate deblurring, which is motivated by distinctive properties of license plate images. For the blurred images, general deblurring methods may restore a good overall visual effect. However, in real-life traffic surveillance system, the deblurring results may be not good for license plates. The main reason lies in that general deblurring methods do not give sufficient thought to the features of license plate, which could be important priors for deblurring. Focusing on this issue, analysis on the statistical distribution characteristics of the license plates are launched, based on which an$L_{p}$-norm-based regularization model is proposed. Furthermore, alternating direction method of multipliers are introduced to solve the model. Experimental results demonstrate that the proposed model performs favorably against the state-of-the-art license plate image deblurring methods.