Coherence Retrieval Using Trace Regularization
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17m1124097.pdf
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Author(s)
Bao, Chenglong
Barbastathis, George
Ji, Hui
Shen, Zuowei
Zhang, Zhengyun
Date Issued
March 2018
Journal
SIAM Journal on Imaging Sciences
Publisher
Society for Industrial and Applied Mathematics
Citation
Bao, Chenglong, et al. “Coherence Retrieval Using Trace Regularization.” SIAM Journal on Imaging Sciences, vol. 11, no. 1, Jan. 2018, pp. 679–706. © 2018 Society for Industrial and Applied Mathematics and by SIAM
Version
Final published version
Abstract
The mutual intensity and its equivalent phase-space representations quantify an optical field's state of coherence and are important tools in the study of light propagation and dynamics, but they can only be estimated indirectly from measurements through a process called coherence retrieval, otherwise known as phase-space tomography. As practical considerations often rule out the availability of a complete set of measurements, coherence retrieval is usually a challenging high-dimensional ill-posed inverse problem. In this paper, we propose a trace-regularized optimization model for coherence retrieval and a provably convergent adaptive accelerated proximal gradient algorithm for solving the resulting problem. Applying our model and algorithm to both simulated and experimental data, we demonstrate an improvement in reconstruction quality over previous models as well as an increase in convergence speed compared to existing first-order methods. Keywords: coherence retrieval, phase-space tomography, trace regularization, adaptive restart
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1137/17M1124097
