论文标题
Deep-url：一种基于深层展开的Richards-Lucy网络的盲目反向扭转的模型感知方法
Deep-URL: A Model-Aware Approach To Blind Deconvolution Based On Deep Unfolded Richardson-Lucy Network
论文作者
论文摘要
当前深度学习模型中缺乏可解释性引起了严重的关注，因为它们被广泛用于各种至关重人的应用。因此，开发可解释的深度学习模型至关重要。在本文中，我们考虑了盲卷曲的问题，并提出了一种新颖的模型感知的深层结构，可以从模糊的图像中恢复模糊内核和清晰的图像。特别是，我们提出了深层展开的理查森·卢西（Richardson-Lucy（Deep-url）框架） - 一种可解释的深度学习结构，可以看作是经典估计技术和深神经网络的合并，因此可以提高性能。我们的数值研究表明，与最先进的算法相比，我们的数字研究显着改善。
The lack of interpretability in current deep learning models causes serious concerns as they are extensively used for various life-critical applications. Hence, it is of paramount importance to develop interpretable deep learning models. In this paper, we consider the problem of blind deconvolution and propose a novel model-aware deep architecture that allows for the recovery of both the blur kernel and the sharp image from the blurred image. In particular, we propose the Deep Unfolded Richardson-Lucy (Deep-URL) framework -- an interpretable deep-learning architecture that can be seen as an amalgamation of classical estimation technique and deep neural network, and consequently leads to improved performance. Our numerical investigations demonstrate significant improvement compared to state-of-the-art algorithms.
