  In the last decades, unsupervised deep learning based methods have caught
researchers attention, since in many real applications, such as medical
imaging, collecting a great amount of training examples is not always feasible.
Moreover, the construction of a good training set is time consuming and hard
because the selected data have to be enough representative for the task. In
this paper, we focus on the Deep Image Prior (DIP) framework and we propose to
combine it with a space-variant Total Variation regularizer with an automatic
estimation of the local regularization parameters. Differently from other
existing approaches, we solve the arising minimization problem via the flexible
Alternating Direction Method of Multipliers (ADMM). Furthermore, we provide a
specific implementation also for the standard isotropic Total Variation. The
promising performances of the proposed approach, in terms of PSNR and SSIM
values, are addressed through several experiments on simulated as well as real
natural and medical corrupted images.
