Blind Deblurring of Single Car Image Based on Differential Autocorrelation and L-like Curve Method

Blind deblurring of single image is an inverse problem in the field of image processing. Its difficulty lies in the non-uniqueness of the inverse process. The regularization method is a classical algorithm for blind deblurring of images, it can iteratively solve blur kernel and clear image by constructing regularization terms. However, improper values of regularization parameter and blur kernel size can easily lead to poor inverse results. In this paper, by calculating differential autocorrelation of the image to obtain the blur scale as the blur kernel size. Then, we proposed a L-like curve method to calculate the regularization parameter. Finally, these calculation results were used to deblur the image with the regularization model. The experimental results show that the proposed method can effectively achieve blind deblurring of images, and the inverse effect of a single blurred car image is obvious. It can meet the requirements of accurately identifying the license plate information of blurred car image. Introduction Single image deblurring is an important research content in the field of image processing. In particular, the blurred image restoration of moving cars has a wide range of needs and applications in traffic video surveillance, traffic violation information verification, and vehicle information recognition. The blind deblurring is the simultaneous solution of clear image and unknown blur kernel. This is a serious inverse problem. The regularization method is a classical method for solving this problem. It was proposed by Tikhonov et al. in 1977, which makes the inverse process tends to a unique solution by regularization constraints. Tikhonov et al. [1] use Gaussian priori to fit the gradient heavy-tailed distribution of natural image, construct a regular term on image gradient, and deblur the image. Although it can suppress noise, the deblurring effect is not good and the details are seriously lost. In order to retain the details of the image, Rudin et al. [2] proposed the Total Variational (TV) regularization method, using Laplacian prior to fit the gradient distribution of natural image, which can get better restoration effect, but it will produce a certain ringing effect. In blind deblurring, the regularization method is used for the estimation of blur kernel. Krishnan et al. [3] proposed standardized sparse priors of image to estimate simple blur kernel. On this basis, Tang et al. [4] firstly filter the image to reduce noise and highlight edges, it can estimate the complex blur kernel. Then, the total variation regularization method is used for non-blind deblurring, but the calculation process is more complex. The regularization parameter is a balance parameter between regular term and fidelity term, and its value directly affects the image restoration results of the regularization method. Generalized Cross Validation (GCV) [5] and L-curve method [6] are the main methods for calculating regularization parameter. GCV method directly uses the raw data with noise to estimate the regularization parameter, and the calculation process is cumbersome. L-curve method takes the weighted form of residual norm and solution norm as the basis for determining regularization parameter. In the iteration process, the search interval of parameter is adjusted according to the degree of ill-posed problem, which is more flexible.