论文标题
通过ADMM结合自然和医疗图像恢复的加权总变化和深层图像
Combining Weighted Total Variation and Deep Image Prior for natural and medical image restoration via ADMM
论文作者
论文摘要
在过去的几十年中，基于深度学习的方法引起了研究人员的关注，因为在许多实际应用中，例如医学成像，收集大量培训示例并不总是可行的。此外，良好的训练集的构建非常耗时且艰难，因为所选数据必须足以代表该任务。在本文中，我们专注于深度图像先验（DIP）框架，并提议将其与空间变化的总变异正常器结合使用，并自动估计局部正则化参数。与其他现有方法不同，我们通过灵活的交替方向方法（ADMM）解决了最小化问题。此外，我们还为标准各向同性总变化提供了特定的实现。从PSNR和SSIM值方面，通过对模拟以及实际的自然和医学损坏的图像进行了几项实验来解决拟议方法的有希望的表演。
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.
