The cookie-related information is fully under our control. These cookies are not used for any purpose other than those described here. Unibo policy
Image denoising has reached impressive heights in performance and quality -- almost as good as it can ever get. This talk is about the many other things one can do with a good denoiser besides using it for its intended purpose. Of particular interest is how to use denoisers in the regularization of any inverse problem. We propose an explicit image-adaptive regularization functional that makes the overall objective functional clear and well-defined. Remarkably, the resulting regularizer is convex. With complete flexibility to choose the iterative optimization procedure for minimizing this functional, RED is capable of incorporating any image denoising algorithm as a regularizer, treat general inverse problems very effectively, and is guaranteed to converge to the globally optimal result. I will show examples of applications, including tone-mapping, deblurring, and super-resolution.
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) .