Provable convergence of plug-and-play priors with MMSE denoisers
Plug-and-play priors (PnP) is a methodology for regularized image reconstruction that
specifies the prior through an image denoiser. While PnP algorithms are well understood for
denoisers performing maximum a posteriori probability (MAP) estimation, they have not
been analyzed for the minimum mean squared error (MMSE) denoisers. This letter
addresses this gap by establishing the first theoretical convergence result for the iterative
shrinkage/thresholding algorithm (ISTA) variant of PnP for MMSE denoisers. We show that …
specifies the prior through an image denoiser. While PnP algorithms are well understood for
denoisers performing maximum a posteriori probability (MAP) estimation, they have not
been analyzed for the minimum mean squared error (MMSE) denoisers. This letter
addresses this gap by establishing the first theoretical convergence result for the iterative
shrinkage/thresholding algorithm (ISTA) variant of PnP for MMSE denoisers. We show that …
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