DualPRNet: Deep Shrinkage Dual Frame Network for Deep Unrolled Phase Retrieval

Abstract

Phase retrieval (PR), i.e., the recovery of the underlying image from the measurements without phase information, is a challenging task, especially at low signal to noise ratios (SNRs). Recent deep unrolling optimizations of tackling this task offer both computational efficiency and high-quality reconstructions. In this work, we involve a novel deep shrinkage network (DSN) into the supervised dual frame learning framework, and propose a deep shrinkage dual frame network dubbed as DualNet for building a deep unrolled PR network architecture. Traditional thresholding functions with hand-crafted thresholds for filtering the frame coefficients are non-adaptive, which limits the final reconstruction quality. Instead, we elaborate a DSN that can learn instance-adaptive and spatial-variant thresholding functions. In a nutshell, we propose the so-called DualPRNet by incorporating the learned dual frames into the unrolled PR framework. Experiments demonstrate that DualPRNet can achieve higher-quality reconstructions compared with previous PR iteration algorithms at low SNRs.