title: Restoration of motion-blurred image based on border deformation detection: a traffic sign restoration model.
abstract: Due to the rapid development of motor vehicle Driver Assistance Systems (DAS), the safety problems associated with automatic driving have become a hot issue in Intelligent Transportation. The traffic sign is one of the most important tools used to reinforce traffic rules. However, traffic sign image degradation based on computer vision is unavoidable during the vehicle movement process. In order to quickly and accurately recognize traffic signs in motion-blurred images in DAS, a new image restoration algorithm based on border deformation detection in the spatial domain is proposed in this paper. The border of a traffic sign is extracted using color information, and then the width of the border is measured in all directions. According to the width measured and the corresponding direction, both the motion direction and scale of the image can be confirmed, and this information can be used to restore the motion-blurred image. Finally, a gray mean grads (GMG) ratio is presented to evaluate the image restoration quality. Compared to the traditional restoration approach which is based on the blind deconvolution method and Lucy-Richardson method, our method can greatly restore motion blurred images and improve the correct recognition rate. Our experiments show that the proposed method is able to restore traffic sign information accurately and efficiently.
authors: Department of Instrument Science and Technology, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, P.R. China.
authors: Yiliang
authors: Y
authors: Zeng
authors: Department of Instrument Science and Technology, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, P.R. China.
authors: Jinhui
authors: J
authors: Lan
authors: School of Transportation, Southeast University, Nanjing, Jiangsu Province, P.R. China.
authors: Bin
authors: B
authors: Ran
authors: Department of Instrument Science and Technology, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, P.R. China.
authors: Qi
authors: Q
authors: Wang
authors: Department of Instrument Science and Technology, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, P.R. China.
authors: Jing
authors: J
authors: Gao
mesh_terms: D000465:Algorithms; D001334:Automobile Driving; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000295:instrumentation*; D008123:Location Directories and Signs; D008962:Models, Theoretical; D009038:Motion; D010363:Pattern Recognition, Automated / Q000379:methods*