The current defuzzy method which only uses the sparse feature of the image as the prior information has poor defuzzy performance induced by ignoring the effect of false edges (such as ring defects) on the point spread function estimation. A regularized image deblurring algorithm with total variation under the constraint of ringing is designed based on the prior information of the complex structure image. Firstly, the multi-resolution image pyramid strategy is adopted to build a multi-layer image model, and the prior information of ringing is obtained by comparing the blurred image with the potentially clear image. Secondly, the ringing regularization constraint term is integrated into the total variation method to build a multi-regularization deblurring model, and then the variable separation method is utilized to transform the deblurring model into a multi-function optimization problem. Finally, the first-order original-dual algorithm is employed to solve the Point Spread Function(PSF) and clear image in the order from low resolution to high resolution. Experimental results show that compared with the current image deblurring technology, the proposed algorithm has a more rational deblurring effect, and the recovered image shows higher peak signal-to-noise ratio and structure similarity, which can better preserve the image edge and texture information.