We propose an effective blind image deblurring method based on multiple priors. Our work is motivated by the fact that a good restored image should favor clear images over blurred images. At present, existing deblurring methods are not ideal for image restoration in specific scenes and there are some blurring, including unclear outlines and details. Aiming at these problems, we propose to combine the prior knowledge of multiple priors, including dark channel priors, intensity priors and gradient priors, and to balance them to provide more priori information for outlines and details during the restoration process. This is of great help for blur kernel estimation. We obtain a total prior knowledge by weighing the three priors, and put it into the maximum a posteriori estimation(MAP) framework. The estimated blur kernel is obtained by iterations, and the original image is restored by using the not blind image restoration method. Our results make great progress compared with current advanced methods, especially the outlines and details in various natural scenarios.