  Phase retrieval algorithms have become an important component in many modern
computational imaging systems. For instance, in the context of ptychography and
speckle correlation imaging, they enable imaging past the diffraction limit and
through scattering media, respectively. Unfortunately, traditional phase
retrieval algorithms struggle in the presence of noise. Progress has been made
recently on more robust algorithms using signal priors, but at the expense of
limiting the range of supported measurement models (e.g., to Gaussian or coded
diffraction patterns). In this work we leverage the regularization-by-denoising
framework and a convolutional neural network denoiser to create prDeep, a new
phase retrieval algorithm that is both robust and broadly applicable. We test
and validate prDeep in simulation to demonstrate that it is robust to noise and
can handle a variety of system models.
  A MatConvNet implementation of prDeep is available at
https://github.com/ricedsp/prDeep.
