Divide and Conquer: Class-adapted Denoisers for Imaging Inverse ProblemsMS52

I will discuss our recent work on patch-based models that are adapted to specific image classes, or even specific scenes. These models take the form of minimum mean squared error (MMSE) patch-based denoisers, using Gaussian mixture priors. We illustrate their use beyond image denoising, in more general inverse problems, such as inpainting, deblurring, and hyperspectral super-resolution, using the recently introduced plug-and-play approach.

This presentation is part of Minisymposium “MS52 - A Denoiser Can Do Much More Than Just... Denoising (2 parts)
organized by: Yaniv Romano (Technion - Israel Institute of Technology) , Peyman Milanfar (Google Research) , Michael Elad (The Technion - Israel Institute of Technology) .

Jose Bioucas-Dias (Universidade de Lisboa, Instituto Superior Técnico (IST), Instituto de Telecomunicações (IT))
Afonso M. Teodoro (Technical University of Lisbon)
Mário Figueiredo (Instituto de Telecomunicações and IST, University of Lisboa)
bayesian methods, image deblurring, image enhancement, image reconstruction, inverse problems