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

TL;DR

This paper introduces a novel image restoration method combining Deep Image Prior with a space-variant Total Variation regularizer, optimized via ADMM, demonstrating improved results on natural and medical images without requiring training data.

Contribution

It proposes a new unsupervised image restoration approach that integrates DIP with space-variant TV regularization and solves it efficiently using ADMM, including a standard TV variant.

Findings

Improved PSNR and SSIM on natural images

Effective on medical image restoration

Competitive with supervised methods

Abstract

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.