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.

HJ-1A satellite is often used to monitor environmental disaster and plays an important role in environmental changes. Because of the affection of various factors, certain band of HJ-1A hyperspectral remote sensing data is severe loss or distortion, which brings great difficulties for subsequent quantitative processing. A novel adaptive window matching algorithm, which can adjust intelligently size of matching window according to different local feature information of the image, is proposed for HJ-1A satellite hyperspectral data recovery in this paper. The results show that the adaptive matching algorithm has a more superior performance than other algorithms in image quality index, column mean curve, and image correlation coefficient.