Image Restoration and Reconstruction using Targeted Plug-and-Play Priors

Leveraging current state-of-the-art denoisers to tackle other inverse problems in imaging is a challenging task, which has recently been the topic of significant research effort. In this paper, we present several contributions to this research front, based on two fundamental building blocks: 1) the recently proposed plug-and-play framework, which allows combining iterative algorithms for imaging inverse problems with state-of-the-art image denoisers, used in black-box fashion; and 2) patch-based denoisers, using Gaussian mixture models (GMM). We exploit the adaptability of GMM to learn class-adapted denoisers, which opens the door to embedding a patch classification step in the algorithmic loop, yielding simultaneous restoration and semantic segmentation. We apply the proposed approach to several standard imaging inverse problems (deblurring, compressive sensing reconstruction, and super-resolution), obtaining results that are competitive with the state of the art.