Abstract
Phase recovery (PR) of a signal from its amplitude measurements is one challenging task in signal processing. The key is suppressing the noise while rectifying the phase of the signal during the inversion process. This letter proposes a deep model-aware approach for PR by unrolling an optimization model regularized with image priors defined in the complex domain. A complex-valued (CV) deep neural network is then introduced to implement effective plug-and-play image priors that enjoy the benefits of CV operations for PR, such as sophisticated operations on local phases and regularization by compact convolution. As a result, the proposed approach can handle the noise well at each iteration in the unrolled process and improve the recovery accuracy. In experiments, the proposed approach shows superior performance to recent methods.
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