TY - GEN
T1 - prDeep
T2 - 35th International Conference on Machine Learning, ICML 2018
AU - Metzler, Christopher A.
AU - Schniter, Philip
AU - Veeraraghavan, Ashok
AU - Baraniuk, Richard G.
N1 - Publisher Copyright:
© The Author(s) 2018.
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85057279221&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85057279221&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85057279221
T3 - 35th International Conference on Machine Learning, ICML 2018
SP - 5654
EP - 5663
BT - 35th International Conference on Machine Learning, ICML 2018
A2 - Dy, Jennifer
A2 - Krause, Andreas
PB - International Machine Learning Society (IMLS)
Y2 - 10 July 2018 through 15 July 2018
ER -