Abstract

We develop a phase retrieval algorithm that utilizes the hybrid-input-output (HIO) algorithm with a deep neural network (DNN). The DNN architecture, which is trained to remove the artifacts of HIO, is used iteratively with HIO to improve the reconstructions. The results demonstrate the effectiveness of the approach with little additional cost.

© 2019 The Author(s)

PDF Article
More Like This
Model-based Phase Retrieval with Deep Denoiser Prior

Çağatay Işıl and Figen S. Oktem
CF2C.5 Computational Optical Sensing and Imaging (COSI) 2020

Deep learning based tomographic phase microscopy with blind structured illumination

Chang Qiao, Hui Qiao, Jiamin Wu, Xiaoxu Li, Jingtao Fan, and Qionghai Dai
NM3C.3 Novel Techniques in Microscopy (NTM) 2019

Physics Embedded Deep Neural Network for Phase Retrieval under Low Photon Conditions

Mo Deng, Alexandre Goy, Kwabena Arthur, and George Barbastathis
CM1A.2 Computational Optical Sensing and Imaging (COSI) 2019

References

You do not have subscription access to this journal. Citation lists with outbound citation links are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access OSA Member Subscription