  In this paper, we propose a new image denoising method, tailored to specific
classes of images, assuming that a dataset of clean images of the same class is
available. Similarly to the non-local means (NLM) algorithm, the proposed
method computes a weighted average of non-local patches, which we interpret
under the importance sampling framework. This viewpoint introduces flexibility
regarding the adopted priors, the noise statistics, and the computation of
Bayesian estimates. The importance sampling viewpoint is exploited to
approximate the minimum mean squared error (MMSE) patch estimates, using the
true underlying prior on image patches. The estimates thus obtained converge to
the true MMSE estimates, as the number of samples approaches infinity.
Experimental results provide evidence that the proposed denoiser outperforms
the state-of-the-art in the specific classes of face and text images.
