Deep Unrolled Phase Retrieval Approach from Coded Diffraction Patterns

Abstract

Phase retrieval (PR) from coded diffraction patterns (CDP) is an inverse problem that consists of estimating a phase image from phaseless measurements acquired with optical systems that include coding masks into the setups. Specifically, CDP contains only magnitude information because current optical sensors cannot measure the phase information of the optical field. Several computational algorithms have been proposed to handle the PR problem. Recently, deep recovery networks in PR have been developed, learning a deep model that acts as a non-linear mapping from CDP to the phase image. However, traditional deep learning approaches in PR lack interpretability in their neural architectures. This paper proposes an unrolling approach where each deep block layer is interpreted as an iteration of a traditional PR algorithm; mainly, the proposed method incorporates two branches used as a proximal operator to estimate magnitude and phase information separately. Simulations show that the proposed unrolled network for PR achieves better reconstruction results at recovering the phase compared to their competitive algorithms.