Deep-Learning Based Regularization of Inverse Problems

基于深度学习的反问题正则化

• 批准号: 464101359
• 负责人: Professor Dr. Martin Burger
• 金额: --
• 依托单位: Projektträger DESY
• 依托单位国家: 德国
• 项目类别: Priority Programmes
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/464101359?language=en
• 关键词: Deep; Learning; Based; Regularization; Inverse

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.

深度学习在图像处理等领域引起了极大的关注，作为反问题的正则化方法也越来越受到人们的关注。尽管有巨大的潜力，但方法的发展，特别是对深层网络在这方面的理解仍处于初级阶段。因此，我们希望推进基于深度学习的病态反问题正则化子的构建及其理论基础。具体的目标是发展健壮性和可解释性的结果，这迫使在这种设置下发展新的健壮性和可解释性的概念。理论发展将伴随着广泛的计算测试，以及为公平比较不同方法而制定的措施和基准问题。