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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Vydané v:IEEE signal processing letters Ročník 24; číslo 12; s. 1872 - 1876
Hlavní autori: Kamilov, Ulugbek S., Mansour, Hassan, Wohlberg, Brendt
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States IEEE 01.12.2017
IEEE Signal Processing Society
Predmet:
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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Popis
Shrnutí: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.
Bibliografia:LA-UR-17-26597
USDOE Laboratory Directed Research and Development (LDRD) Program
AC52-06NA25396
ISSN:1070-9908
1558-2361
DOI:10.1109/LSP.2017.2763583