UPR: A Model-Driven Architecture for Deep Phase Retrieval
This work addresses phase retrieval, a long-standing problem in signal processing with broad applications, but it appears incremental as it builds on existing methods.
The paper tackles the phase retrieval problem by proposing a hybrid model-based data-driven deep architecture called Unfolded Phase Retrieval (UPR), which improves the performance of state-of-the-art algorithms by combining the interpretability of model-based methods with the expressive power of deep neural networks, though no concrete numbers are provided.
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.