Deep-Learning Based Regularization of Inverse Problems

深度学习在图像处理等许多领域引起了极大的关注，因此它作为一种正则化逆问题的方法受到越来越多的关注。尽管具有巨大的潜力，但方法的发展，特别是对这方面深度网络的理解仍处于起步阶段。因此，我们希望推进基于深度学习的不适定逆问题正则化器的构建及其理论基础。特定的目标是开发鲁棒性和可解释性的结果，这要求在这种设置中开发鲁棒性和可解释性的新概念。在进行理论发展的同时，将进行广泛的计算试验，并制定各种措施和基准问题，以便公平比较不同的方法。

Deep learning has attracted enormous attention in many fields like image processing and consequently it receives growing interest as a method to regularize inverse problems. Despite its great potential, the development of methods and in particular the understanding of deep networks in this respect is still in its infancy. We hence want to advance the construction of deep-learning based regularizers for ill-posed inverse problems and their theoretical foundations.Particular goals are the development of robust and interpretable results, which enforce to develop novel concepts of robustness and interpretability in this setup. The theoretical developments will be accompanied by extensive computational tests and the development of measures and benchmark problems for fair comparison of different approaches.