DualPRNet: Deep Shrinkage Dual Frame Network for Deep Unrolled Phase Retrieval
Baoshun Shi, Qiusheng Lian
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.