The edge is an important feature of images,which is well used in many practical engineering fields,such as edge detection,object recognition and object tracking.However,the i mages often suffer from the blur effect due to motion between the object and the camera during the image acquisition pro cess.Numerous methods have been proposed for this problem,and the total variation based methods are the most po pular and efficient ones.But,images deblurred by using total variation based methods often suffer from serious st air-casing effects,which would damage the quality of the corners on the edge of images.In this paper,to protect the corn ers of the objects in images,an anisotropic total variation based regularization method is proposed for blind image deblurri ng,which regularizes both image and blur kernel by using anisotropic total variation instead of total variation.Bes ides,efficient iterative formula based on alternative method and split Bregman iteration is derived to apply the improved method in numerical experiments. Moreover,the results reveal that compared with the total variation based meth ods and framelet methods,the presented method can estimate the precise point spread function (PSF) an d remove the blurring as well as protecting the details of corners of objects.