A Denoiser Can Do Much More Than Just... DenoisingMS52

The task of image denoising is extensively studied for decades. A recent advance in this field results in dazzling results, so much so that some researchers believe that working on this problem leads to a dead avenue. Is this truly the case? What are the new trends in this field? Deep learning? New optimization methods? Can we leverage this impressive achievement and tackle other restoration problems, pushing these to new heights? All of these questions and more are the matter of this minisymposium.

Regularization by Denoising (RED): New and surprising uses of an old problem
Peyman Milanfar (Google Research)
Fast Algorithms for Regularization-by-Denoising
Phil Schniter (The Ohio State University)
Image Restoration by Iterative Denoising and Backward Projections
Raja Giryes (Tel Aviv University)
Divide and Conquer: Class-adapted Denoisers for Imaging Inverse Problems
Mário Figueiredo (Instituto de Telecomunicações and IST, University of Lisboa)
Learning to Mean-Shift in O(1) for Bayesian Image Restoration
Siavash Arjomand Bigdeli (EPFL)
Connect Maximum A Posteriori (MAP) Inference with Convolutional Neural Network for Image Restoration
Wangmeng Zuo (Harbin Institute of Technology)
On the Confluence of Deep Learning and Inverse Problems
Daniel Cremers (Technische Universität München)
Michael Elad (The Technion - Israel Institute of Technology)
Peyman Milanfar (Google Research)
Yaniv Romano (Technion - Israel Institute of Technology)
deep learning, image deblurring, image reconstruction, inverse problems, nonlinear optimization