In a blind image de-blurring,estimating a blur kernel from a single blurred image is a severely ill-posed problem.In this paper,a blind image de-blurring method based on sparse prior and relative total variation is pro- posed.In this method,a smoothing algorithm based on weighted L0 is employed to adaptively extract the main structure of an image and remove such factors adverse to a kernel estimation as the noise,the details and the edges of a small object,and a relative total variation method is adopted to overcome the inaccuracy of estimating a com- plex blur kernel by means of the regularization method of the sparse prior.Moreover,the regularization method of the Super-Laplacian prior is used to estimate the latent image.Experimental results show that，as compared with the existing image de-blurring method,the proposed method helps achieve the latent image of better structure and less artifacts,and it can better recover the latent image.