prDeep: Robust Phase Retrieval with a Flexible Deep Network

Christopher Metzler, Phillip Schniter, Ashok Veeraraghavan, Richard Baraniuk
Proceedings of the 35th International Conference on Machine Learning, PMLR 80:3501-3510, 2018.

Abstract

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 developing 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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v80-metzler18a, title = {pr{D}eep: Robust Phase Retrieval with a Flexible Deep Network}, author = {Metzler, Christopher and Schniter, Phillip and Veeraraghavan, Ashok and Baraniuk, Richard}, booktitle = {Proceedings of the 35th International Conference on Machine Learning}, pages = {3501--3510}, year = {2018}, editor = {Dy, Jennifer and Krause, Andreas}, volume = {80}, series = {Proceedings of Machine Learning Research}, month = {10--15 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v80/metzler18a/metzler18a.pdf}, url = {https://proceedings.mlr.press/v80/metzler18a.html}, abstract = {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 developing 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.} }
Endnote
%0 Conference Paper %T prDeep: Robust Phase Retrieval with a Flexible Deep Network %A Christopher Metzler %A Phillip Schniter %A Ashok Veeraraghavan %A Richard Baraniuk %B Proceedings of the 35th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2018 %E Jennifer Dy %E Andreas Krause %F pmlr-v80-metzler18a %I PMLR %P 3501--3510 %U https://proceedings.mlr.press/v80/metzler18a.html %V 80 %X 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 developing 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.
APA
Metzler, C., Schniter, P., Veeraraghavan, A. & Baraniuk, R.. (2018). prDeep: Robust Phase Retrieval with a Flexible Deep Network. Proceedings of the 35th International Conference on Machine Learning, in Proceedings of Machine Learning Research 80:3501-3510 Available from https://proceedings.mlr.press/v80/metzler18a.html.

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