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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) .