Papers › On the Strong Convexity of PnP Regularization Using Linear Denoisers
On the Strong Convexity of PnP Regularization Using Linear Denoisers
Arghya Sinha, Kunal N Chaudhury
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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 𝒫. For such a linear denoiser, we prove that 𝒫 exhibits strong convexity for linear inverse problems. Specifically, we show that the strong convexity of 𝒫 can be used to certify objective and iterative convergence of any PnP algorithm derived from classical proximal methods.
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