Blind image deblurring remains a topic of enduring interest. Learning based approaches, especially those that employ neural networks have emerged to complement traditional model based methods and in many cases achieve vastly enhanced performance. That said, neural network approaches are generally empirically designed and the underlying structures are difficult to interpret. In recent years, a promising technique called algorithm unrolling has been developed that has helped connect iterative algorithms such as those for sparse coding to neural network architectures. In this article, we propose a neural network architecture based on this idea. We first present an iterative algorithm that may be considered as a generalization of the traditional total-variation regularization method in the gradient domain. We then unroll the algorithm to construct a neural network for image deblurring which we refer to as Deep Unrolling for Blind Deblurring (DUBLID). Key algorithm parameters are learned with the help of training images. Our proposed deep network DUBLID achieves significant practical performance gains while enjoying interpretability and efficiency at the same time. Extensive experimental results show that DUBLID outperforms many state-of-the-art methods and in addition is computationally faster.

中文翻译：

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通过算法展开有效且可解释的深度盲图像去模糊

盲图像去模糊仍然是一个持久感兴趣的话题。基于学习的方法，尤其是那些采用神经网络的方法，已经出现，以补充传统的基于模型的方法，并在许多情况下实现了极大的性能提升。也就是说，神经网络方法通常是凭经验设计的，底层结构很难解释。近年来，已经开发出一种称为算法展开的有前途的技术，该技术有助于将迭代算法（例如用于稀疏编码的算法）连接到神经网络架构。在本文中，我们提出了基于此思想的神经网络架构。我们首先提出了一种迭代算法，可以将其视为梯度域中传统全变分正则化方法的推广。然后我们展开算法以构建用于图像去模糊的神经网络，我们将其称为盲去模糊深度展开（DUBLID）。在训练图像的帮助下学习关键算法参数。我们提出的深度网络 DUBLID 在获得可解释性和效率的同时实现了显着的实际性能提升。大量的实验结果表明，DUBLID 优于许多最先进的方法，而且计算速度更快。