skip to main content
DOE PAGES title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: A Plug-and-Play Priors Approach for Solving Nonlinear Imaging Inverse Problems

Abstract

In the past two decades, nonlinear image reconstruction methods have led to substantial improvements in the capabilities of numerous imaging systems. Such methods are traditionally formulated as optimization problems that are solved iteratively by simultaneously enforcing data consistency and incorporating prior models. Recently, the Plug-and-Play Priors (PPP) framework suggested that by using more sophisticated denoisers, not necessarily corresponding to an optimization objective, it is possible to improve the quality of reconstructed images. Here in this letter, we show that the PPP approach is applicable beyond linear inverse problems. In particular, we develop the fast iterative shrinkage/thresholding algorithm variant of PPP for model-based nonlinear inverse scattering. The key advantage of the proposed formulation over the original ADMM-based one is that it does not need to perform an inversion on the forward model. We show that the proposed method produces high quality images using both simulated and experimentally measured data.

Authors:
ORCiD logo [1];  [1]; ORCiD logo [2]
  1. Mitsubishi Electric Research Lab. (MERL), Cambridge, MA (United States)
  2. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Publication Date:
Research Org.:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
USDOE Laboratory Directed Research and Development (LDRD) Program
OSTI Identifier:
1469540
Report Number(s):
LA-UR-17-26597
Journal ID: ISSN 1070-9908
Grant/Contract Number:  
AC52-06NA25396
Resource Type:
Accepted Manuscript
Journal Name:
IEEE Signal Processing Letters
Additional Journal Information:
Journal Volume: 24; Journal Issue: 12; Journal ID: ISSN 1070-9908
Publisher:
IEEE Signal Processing Society
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; Information Science; Mathematics; Fast iterative shrinkage/thresholding algorithm (FISTA); image reconstruction; inverse scattering; nonlinear inverse problems; plug-and-play priors (PPP)

Citation Formats

Kamilov, Ulugbek S., Mansour, Hassan, and Wohlberg, Brendt. A Plug-and-Play Priors Approach for Solving Nonlinear Imaging Inverse Problems. United States: N. p., 2017. Web. doi:10.1109/LSP.2017.2763583.
Kamilov, Ulugbek S., Mansour, Hassan, & Wohlberg, Brendt. A Plug-and-Play Priors Approach for Solving Nonlinear Imaging Inverse Problems. United States. doi:10.1109/LSP.2017.2763583.
Kamilov, Ulugbek S., Mansour, Hassan, and Wohlberg, Brendt. Fri . "A Plug-and-Play Priors Approach for Solving Nonlinear Imaging Inverse Problems". United States. doi:10.1109/LSP.2017.2763583. https://www.osti.gov/servlets/purl/1469540.
@article{osti_1469540,
title = {A Plug-and-Play Priors Approach for Solving Nonlinear Imaging Inverse Problems},
author = {Kamilov, Ulugbek S. and Mansour, Hassan and Wohlberg, Brendt},
abstractNote = {In the past two decades, nonlinear image reconstruction methods have led to substantial improvements in the capabilities of numerous imaging systems. Such methods are traditionally formulated as optimization problems that are solved iteratively by simultaneously enforcing data consistency and incorporating prior models. Recently, the Plug-and-Play Priors (PPP) framework suggested that by using more sophisticated denoisers, not necessarily corresponding to an optimization objective, it is possible to improve the quality of reconstructed images. Here in this letter, we show that the PPP approach is applicable beyond linear inverse problems. In particular, we develop the fast iterative shrinkage/thresholding algorithm variant of PPP for model-based nonlinear inverse scattering. The key advantage of the proposed formulation over the original ADMM-based one is that it does not need to perform an inversion on the forward model. We show that the proposed method produces high quality images using both simulated and experimentally measured data.},
doi = {10.1109/LSP.2017.2763583},
journal = {IEEE Signal Processing Letters},
number = 12,
volume = 24,
place = {United States},
year = {2017},
month = {12}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record

Citation Metrics:
Cited by: 6 works
Citation information provided by
Web of Science

Save / Share: