Single-scale motion blur kernel estimation method based on continuous double frames

In the scene of recognizing the driving posture or behavior of the driver, the problem of motion blur in the image frame usually affects the recognition result. Because of the long calculation time of the existing multi-scale fuzzy kernel estimation method, it is not completely suitable for the driver's cab recognition scene. Therefore, based on the characteristics of the motion blur image in the cab scene, this paper proposes a single-scale motion blur kernel estimation method based on continuous double frames. First of all, in order to accurately extract the effective edge information of the blurred image, this paper improves the edge extraction effect by extracting the edge gradient information without crossing. Secondly, according to the characteristics of the motion blur kernel in the cab scene, this paper proposes to construct a new fuzzy kernel calculation model based on the information of the continuous two-frame blurred image. The method in this paper aims to use as much known information as possible to ensure the accuracy of the fuzzy kernel and reduce the calculation time of the fuzzy kernel estimation. Experiments have proved that the method in this paper can not only reduce the calculation time, but also achieve comparable or even better deblurring effects.