Recent frameworks, such as the so-called plug-and-play, allow us to leverage the developments in image denoising to tackle other, and more involved, problems in image processing. As the name suggests, state-of-the-art denoisers are plugged into an iterative algorithm that alternates between a denoising step and the inversion of the observation operator. While these tools offer flexibility, the convergence of the resulting algorithm may be difficult to analyse. In this paper, we plug a state-of-the-art denoiser, based on a Gaussian mixture model, in the iterations of an alternating direction method of multipliers and prove the algorithm is guaranteed to converge. Moreover, we build upon the concept of scene-adapted priors where we learn a model targeted to a specific scene being imaged, and apply the proposed method to address the hyperspectral sharpening problem.
@article{arxiv.1702.02445,
title = {Scene-adapted plug-and-play algorithm with convergence guarantees},
author = {Afonso M. Teodoro and José M. Bioucas-Dias and Mário A. T. Figueiredo},
journal= {arXiv preprint arXiv:1702.02445},
year = {2017}
}