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Fixed-Point and Objective Convergence of Plug-and-Play Algorithms

Nair, P and Gavaskar, RG and Chaudhury, KN (2021) Fixed-Point and Objective Convergence of Plug-and-Play Algorithms. In: IEEE Transactions on Computational Imaging, 7 . pp. 337-348.

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Official URL: https://doi.org/10.1109/TCI.2021.3066053


A standard model for image reconstruction involves the minimization of a data-fidelity term along with a regularizer, where the optimization is performed using proximal algorithms such as ISTA and ADMM. In plug-and-play (PnP) regularization, the proximal operator (associated with the regularizer) in ISTA and ADMM is replaced by a powerful image denoiser. Although PnP regularization works surprisingly well in practice, its theoretical convergence - whether convergence of the PnP iterates is guaranteed and if they minimize some objective function - is not completely understood even for simple linear denoisers such as nonlocal means. In particular, while there are works where either iterate or objective convergence is established separately, a simultaneous guarantee on iterate and objective convergence is not available for any denoiser to our knowledge. In this paper, we establish both forms of convergence for a special class of linear denoisers. Notably, unlike existing works where the focus is on symmetric denoisers, our analysis covers non-symmetric denoisers such as nonlocal means and almost any convex data-fidelity. The novelty in this regard is that we make use of the convergence theory of averaged operators and we work with a special inner product (and norm) derived from the linear denoiser; the latter requires us to appropriately define the gradient and proximal operators associated with the data-fidelity term. We validate our convergence results using image reconstruction experiments.

Item Type: Journal Article
Publication: IEEE Transactions on Computational Imaging
Publisher: Institute of Electrical and Electronics Engineers Inc.
Additional Information: The copyright for this article belongs to the Institute of Electrical and Electronics Engineers Inc.
Keywords: Averaged operator; convergence; image reconstruction; linear denoiser; proximal operator; regularization
Department/Centre: Division of Electrical Sciences > Electrical Engineering
Date Deposited: 28 Oct 2023 02:58
Last Modified: 28 Oct 2023 02:58
URI: https://eprints.iisc.ac.in/id/eprint/82782

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