On the Strong Convexity of PnP Regularization Using Linear Denoisers

Fuente: arXiv
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Main Authors: Sinha, Arghya, Chaudhury, Kunal N
Format: Preprint
Published: 2024
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author Sinha, Arghya
Chaudhury, Kunal N
author_facet Sinha, Arghya
Chaudhury, Kunal N
contents In the Plug-and-Play (PnP) method, a denoiser is used as a regularizer within classical proximal algorithms for image reconstruction. It is known that a broad class of linear denoisers can be expressed as the proximal operator of a convex regularizer. Consequently, the associated PnP algorithm can be linked to a convex optimization problem $\mathcal{P}$. For such a linear denoiser, we prove that $\mathcal{P}$ exhibits strong convexity for linear inverse problems. Specifically, we show that the strong convexity of $\mathcal{P}$ can be used to certify objective and iterative convergence of any PnP algorithm derived from classical proximal methods.
format Preprint
id arxiv_https___arxiv_org_abs_2411_01027
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle On the Strong Convexity of PnP Regularization Using Linear Denoisers
Sinha, Arghya
Chaudhury, Kunal N
Optimization and Control
Image and Video Processing
In the Plug-and-Play (PnP) method, a denoiser is used as a regularizer within classical proximal algorithms for image reconstruction. It is known that a broad class of linear denoisers can be expressed as the proximal operator of a convex regularizer. Consequently, the associated PnP algorithm can be linked to a convex optimization problem $\mathcal{P}$. For such a linear denoiser, we prove that $\mathcal{P}$ exhibits strong convexity for linear inverse problems. Specifically, we show that the strong convexity of $\mathcal{P}$ can be used to certify objective and iterative convergence of any PnP algorithm derived from classical proximal methods.
title On the Strong Convexity of PnP Regularization Using Linear Denoisers
topic Optimization and Control
Image and Video Processing
url https://arxiv.org/abs/2411.01027