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We propose an <inline-formula> <tex-math notation="LaTeX">$L_{p}$ </tex-math></inline-formula>-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 <inline-formula> <tex-math notation="LaTeX">$L_{p}$ </tex-math></inline-formula>-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.

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