In many fields, images become a useful tool containing data of which medical image is an example. The diagnosis depends on the skills of the doctors and image clarity. In the real world, most of medical images consist of noise and blur. This problem reduces the quality of images and causes difficulties for doctors. Most of the tasks of increasing the quality of medical images are deblurring or denoising process. This is the difficult problem in medical image processing, because it must keep the edge features and avoid the loss of information. In case of a medical image which contains noise combined with blur, it is more difficult. In this paper, we have proposed a method for increasing the quality of medical images in case that blur combined with noise pair is available in medical images. The proposed method is divided into two steps: denoising and deblurring. We use curvelet transform combined with bayesian thresholding for the denoising step and use the augmented lagrangian method for the deblurring step. For demonstrating the superiority of the proposed method, we have compared the results with the other recent methods available in literature.