Licence plate images deblurring with binarization threshold

The principal purpose of this paper is to develop a new method to deblur licence plate images. The statistical analyses of plate images we have performed enable us to believe that the binarization threshold is a reasonable parameter to distinguish blurred plate images from clean ones. Our approach defines a new regularization term which includes both intensity and gradient priors, and gives an effective and convergent solution. A large number of experiments on real-world plate images dataset have been conducted by using different algorithms. Comparing with other representative deblur algorithms, the method we proposed yields higher quality results. Moreover, further experiments are carried out to apply our algorithm to non-plate blurred images, such as composite images, saturated images and other common images. The results demonstrate the our method has a state-of-the-art performance on both document and non-document images.