Generalized proximal smoothing (GPS) for phase retrieval

Pham, Minh; Yin, Penghang; Rana, Arjun; Osher, Stanley; Miao, Jianwei

doi:10.1364/oe.27.002792

Title: Generalized proximal smoothing (GPS) for phase retrieval

Journal Article · 29 January 2019 · Optics Express

DOI: https://doi.org/10.1364/oe.27.002792 · OSTI ID:1492877

^[1]; Yin, Penghang ^[1]; Rana, Arjun ^[1]; Osher, Stanley ^[1]; Miao, Jianwei ^[1]

Univ. of California, Los Angeles, CA (United States)

In this paper, we report the development of the generalized proximal smoothing (GPS) algorithm for phase retrieval of noisy data. GPS is a optimization-based algorithm, in which we relax both the Fourier magnitudes and support constraint. We relax the support constraint by incorporating the Moreau-Yosida regularization and heat kernel smoothing, and derive the associated proximal mapping. We also relax the magnitude constraint into a least squares fidelity term, whose proximal mapping is available as well. GPS alternatively iterates between the two proximal mappings in primal and dual spaces, respectively. Using both numerical simulation and experimental data, we show that GPS algorithm consistently outperforms the classical phase retrieval algorithms such as hybrid input-output (HIO) and oversampling smoothness (OSS), in terms of the convergence speed, consistency of the phase retrieval, and robustness to noise.

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Research Organization:: Univ. of California, Los Angeles, CA (United States)

Sponsoring Organization:: USDOE Office of Science (SC)

Grant/Contract Number:: SC0013838

OSTI ID:: 1492877

Alternate ID(s):: OSTI ID: 1612034

Journal Information:: Optics Express, Vol. 27, Issue 3; ISSN 1094-4087

Publisher:: Optical Society of America (OSA)Copyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 7 works

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