Physics

Pattern recognition (psychology)

Mathematics

Image (mathematics)

Optics

Image restoration

Mathematical optimization

Machine learning

Minification

Artificial intelligence

Management

Economics

Total variation denoising

Regularization (linguistics)

Focus (optics)

Programming language

Variation (astronomy)

Computer science

Astrophysics

Task (project management)

Image processing

In the last decades, unsupervised deep learning based methods have caught researchers' attention, since in many real applications, such as medical imaging, collecting a large 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.

Combining Weighted Total Variation and Deep Image Prior for natural and medical image restoration via ADMM