We organize a minisymposium (2 parts) at SIAM IS 22 with Luca Calatroni and Paul Escande:
Non-Convex Optimization Methods for Inverse Problems in Imaging: From Theory to Applications
Come join us on Thursday, March 24, 2022!
Abstract:
Over the past decade, there has been a growing interest in the imaging community to use non-convex sparse optimization methods. These approaches are now ubiquitous in a plethora of real-world applications. Many recent theoretical contributions have proven the success of these methods. With respect to sparse regularization models, non-convexity arises naturally when dealing with efficient approximations to the pseudo-normality l_0 and/or when dealing with joint optimization problems where non-convexity is the by-product of a cross-regularization term and/or non-convex data models. The objective of this minisymposium is to gather experts in the field of non-convex regularization methods for inverse imaging problems to provide an overview of the field ranging from recent theoretical results to the design of numerical optimization methods that could be used effectively in a variety of applications.
With respect to theoretical developments, this minisymposium will focus on convergence guarantees and derivation of convergence rates of non-convex methods and their relation to the specific structure of imaging problems and low-dimensional models. A selection of contributions dealing with the design of efficient algorithms for new non-convex formulations of imaging problems will then be presented. Finally, some presentations on the actual use of these methods in real applications such as microscopic imaging, medical imaging and sparse signal recovery will be given.