In the last decades, unsupervised deep learning based methods have caught researchers attention, since in many applications 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 mainly focus on the Deep Image Prior (DIP) framework powered by adding the Total Variation regularizer which promotes gradient-sparsity of the solution . Differently from other existing approaches, we solve the arising minimization problem by using the well known Alternating Direction Method of Multipliers (ADMM) framework, decoupling the contribution of the DIP L_{2 . The promising performances of the proposed approach, in terms of PSNR and SSIM values, are addressed by means of experiments for different image restoration tasks on synthetic as well as on real data .
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 .
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applications
real applications, such as medical imaging,
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mainly
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powered by adding the
and we propose to combine it with a space-variant
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which promotes gradient-sparsity of the solution
with an automatic estimation of the local regularization parameters
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by using the well known
via the flexible
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framework, decoupling the contribution of the DIP L_{2
. Furthermore, we provide a specific implementation also for the standard isotropic Total Variation
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by means of experiments for different image restoration tasks on synthetic
through several experiments on simulated
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on real data
real natural and medical corrupted images