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

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 incorp...

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Vydáno v:	IEEE signal processing letters Ročník 24; číslo 12; s. 1872 - 1876
Hlavní autoři:	Kamilov, Ulugbek S., Mansour, Hassan, Wohlberg, Brendt
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States IEEE 01.12.2017 IEEE Signal Processing Society
Témata:	Computational modeling Fast iterative shrinkage/thresholding algorithm (FISTA) image reconstruction Imaging Information Science Inverse problems inverse scattering Iterative methods Mathematical model Mathematics MATHEMATICS AND COMPUTING Noise measurement nonlinear inverse problems Optimization plug-and-play priors (PPP) Scattering
ISSN:	1070-9908, 1558-2361
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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. 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.
AbstractList	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. 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. 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.
Author	Mansour, Hassan Kamilov, Ulugbek S. Wohlberg, Brendt
Author_xml	– sequence: 1 givenname: Ulugbek S. surname: Kamilov fullname: Kamilov, Ulugbek S. email: kamilov@wustl.edu organization: Comput. Imaging Group, Washington Univ. in St. Louis, St. Louis, MO, USA – sequence: 2 givenname: Hassan surname: Mansour fullname: Mansour, Hassan email: mansour@merl.com organization: Mitsubishi Electr. Res. Labs., Cambridge, MA, USA – sequence: 3 givenname: Brendt surname: Wohlberg fullname: Wohlberg, Brendt email: brendt@lanl.gov organization: Theor. Div., Los Alamos Nat. Lab., Los Alamos, NM, USA
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SubjectTerms	Computational modeling Fast iterative shrinkage/thresholding algorithm (FISTA) image reconstruction Imaging Information Science Inverse problems inverse scattering Iterative methods Mathematical model Mathematics MATHEMATICS AND COMPUTING Noise measurement nonlinear inverse problems Optimization plug-and-play priors (PPP) Scattering
Title	A Plug-and-Play Priors Approach for Solving Nonlinear Imaging Inverse Problems
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