An Online Plug-and-Play Algorithm for Regularized Image Reconstruction

Plug-and-play priors (PnP) is a powerful framework for regularizing imaging inverse problems by using advanced denoisers within an iterative algorithm. Recent experimental evidence suggests that PnP algorithms achieve the state-of-the-art performance in a range of imaging applications. In this paper...

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Bibliographic Details
Published in:IEEE transactions on computational imaging 2019-09, Vol.5 (3), p.395-408
Main Authors: Sun, Yu, Wohlberg, Brendt, Kamilov, Ulugbek S.
Format: Article
Language:English
Subjects:
Algorithms
alternating minimization
Computer simulation
Convergence
Datasets
Image reconstruction
Imaging
Inverse problems
Iterative algorithms
iterative thresholding
MATHEMATICS AND COMPUTING
Noise measurement
Noise reduction
Optimization
plug-and-play priors
plug-andplay priors
regularization by denoising
Regularized image reconstruction
stochastic optimization
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Summary:Plug-and-play priors (PnP) is a powerful framework for regularizing imaging inverse problems by using advanced denoisers within an iterative algorithm. Recent experimental evidence suggests that PnP algorithms achieve the state-of-the-art performance in a range of imaging applications. In this paper, we introduce a new online PnP algorithm based on the proximal gradient method (PGM). The proposed algorithm uses only a subset of measurements at every iteration, which makes it scalable to very large datasets. We present a new theoretical convergence analysis, for both batch and online variants of PnP-PGM, for denoisers that do not necessarily correspond to proximal operators. We also present simulations illustrating the applicability of the algorithm to image reconstruction in diffraction tomography. The results in this paper have the potential to expand the applicability of the PnP framework to very large datasets.
ISSN:2573-0436
2333-9403
DOI:10.1109/TCI.2019.2893568