TY - GEN
T1 - When deep denoising meets iterative phase retrieval
AU - Wang, Yaotian
AU - Sun, Xiaohang
AU - Fleischer, Jason W.
N1 - Funding Information:
This material is based upon work supported by the Air Force Office of Scientific Research (AFOSR) under Grant FA9550-18-1-0219 and by the Defense Advanced Research Projects Agency (DARPA) under Agreement No. HR00111890042.
Publisher Copyright:
© 2020 by the Authors.
PY - 2020
Y1 - 2020
N2 - Recovering a signal from its Fourier intensity underlies many important applications, including lensless imaging and imaging through scattering media. Conventional algorithms for retrieving the phase suffer when noise is present but display global convergence when given clean data. Neural networks have been used to improve algorithm robustness, but efforts to date are sensitive to initial conditions and give inconsistent performance. Here, we combine iterative methods from phase retrieval with image statistics from deep denoisers, via regularization-by-denoising. The resulting methods inherit the advantages of each approach and outperform other noise-robust phase retrieval algorithms. Our work paves the way for hybrid imaging methods that integrate machinelearned constraints in conventional algorithms.
AB - Recovering a signal from its Fourier intensity underlies many important applications, including lensless imaging and imaging through scattering media. Conventional algorithms for retrieving the phase suffer when noise is present but display global convergence when given clean data. Neural networks have been used to improve algorithm robustness, but efforts to date are sensitive to initial conditions and give inconsistent performance. Here, we combine iterative methods from phase retrieval with image statistics from deep denoisers, via regularization-by-denoising. The resulting methods inherit the advantages of each approach and outperform other noise-robust phase retrieval algorithms. Our work paves the way for hybrid imaging methods that integrate machinelearned constraints in conventional algorithms.
UR - http://www.scopus.com/inward/record.url?scp=85100281101&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85100281101&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85100281101
T3 - 37th International Conference on Machine Learning, ICML 2020
SP - 9949
EP - 9959
BT - 37th International Conference on Machine Learning, ICML 2020
A2 - Daume, Hal
A2 - Singh, Aarti
PB - International Machine Learning Society (IMLS)
T2 - 37th International Conference on Machine Learning, ICML 2020
Y2 - 13 July 2020 through 18 July 2020
ER -