  State-of-the-art algorithms for imaging inverse problems (namely deblurring
and reconstruction) are typically iterative, involving a denoising operation as
one of its steps. Using a state-of-the-art denoising method in this context is
not trivial, and is the focus of current work. Recently, we have proposed to
use a class-adapted denoiser (patch-based using Gaussian mixture models) in a
so-called plug-and-play scheme, wherein a state-of-the-art denoiser is plugged
into an iterative algorithm, leading to results that outperform the best
general-purpose algorithms, when applied to an image of a known class (e.g.
faces, text, brain MRI). In this paper, we extend that approach to handle
situations where the image being processed is from one of a collection of
possible classes or, more importantly, contains regions of different classes.
More specifically, we propose a method to locally select one of a set of
class-adapted Gaussian mixture patch priors, previously estimated from clean
images of those classes. Our approach may be seen as simultaneously performing
segmentation and restoration, thus contributing to bridging the gap between
image restoration/reconstruction and analysis.
