  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.
