In this work we explore the previously proposed approach of direct blind deconvolution and denoising with convolutional neural networks in a situation where the blur kernels are partially constrained. We focus on blurred images from a real-life traffic surveillance system, on which we, for the first time, demonstrate that neural networks trained on artificial data provide superior reconstruction quality on real images compared to traditional blind deconvolution methods. The training data is easy to obtain by blurring sharp photos from a target system with a very rough approximation of the expected blur kernels, thereby allowing custom CNNs to be trained for a specific application (image content and blur range). Additionally, we evaluate the behavior and limits of the CNNs with respect to blur direction range and length.
 Nowadays, drive recorders are becoming a popular form of evidences used by drivers and accepted by court. One common investigation task is to identify vehicles of interest and recognize their license plates (LPs). In this paper, we focus on License Plate Recognition (LPR) based on single snapshot from a drive recorder. As drive recorders are installed on moving vehicles, snapshots by drive recorders usually suffer from serious blur, and the key issue is recognizing the Blurred License Plate (BLP) from single image. A straightforward method is first deblurring the BLP and then recognizing it. However, the first problem with this method is that general image deblurring methods are designed to get a good overall visual effect and the deblurred results may be not good for LPR. The second problem is that general image deblurring methods don't use the features of the LPs, which could be important priors for the deblurring process. To overcome these issues, this paper proposes a novel method that integrates deblurring and recognizing in a closed-loop. The proposed method utilizes characters and patterns of LPs as priors, and the deblurring and recognizing process will stop when a reliable recognition result is obtained from the deblurred image. Furthermore, by analyzing the features of BLPs, this paper proposes a l 0 -norm based deblurring method. Experiments show that, compared to other LPR methods, the proposed method can achieve higher recognition rate on the BLPs. 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.