论文标题
UPR：用于深阶段检索的模型驱动架构
UPR: A Model-Driven Architecture for Deep Phase Retrieval
论文作者
论文摘要
几十年来，由于其在广泛的应用中出现，因此相位检索的问题一直吸引了研究人员。相位检索算法的任务通常是从无线性相位测量中恢复信号。在本文中，我们通过提出一种基于混合模型的数据驱动的深度体系结构（称为展开的相位检索（UPR））来解决问题，该体系结构（UPR）表明了提高最先进的阶段检索算法的性能的潜力。具体而言，所提出的方法受益于基于模型的算法的多功能性和解释性，同时从深层神经网络的表现力中受益。我们的数值结果说明了这种混合深度体系结构的有效性，并展示了数据辅助方法的未开发潜力，以增强现有的相位检索算法。
The problem of phase retrieval has been intriguing researchers for decades due to its appearance in a wide range of applications. The task of a phase retrieval algorithm is typically to recover a signal from linear phase-less measurements. In this paper, we approach the problem by proposing a hybrid model-based data-driven deep architecture, referred to as the Unfolded Phase Retrieval (UPR), that shows potential in improving the performance of the state-of-the-art phase retrieval algorithms. Specifically, the proposed method benefits from versatility and interpretability of well established model-based algorithms, while simultaneously benefiting from the expressive power of deep neural networks. Our numerical results illustrate the effectiveness of such hybrid deep architectures and showcase the untapped potential of data-aided methodologies to enhance the existing phase retrieval algorithms.
