title: A majorize-minimize strategy for subspace optimization applied to image restoration.
abstract: This paper proposes accelerated subspace optimization methods in the context of image restoration. Subspace optimization methods belong to the class of iterative descent algorithms for unconstrained optimization. At each iteration of such methods, a stepsize vector allowing the best combination of several search directions is computed through a multidimensional search. It is usually obtained by an inner iterative second-order method ruled by a stopping criterion that guarantees the convergence of the outer algorithm. As an alternative, we propose an original multidimensional search strategy based on the majorize-minimize principle. It leads to a closed-form stepsize formula that ensures the convergence of the subspace algorithm whatever the number of inner iterations. The practical efficiency of the proposed scheme is illustrated in the context of edge-preserving image restoration.
authors.affiliation: IRCCyN (CNRS UMR 6597), Ecole Centrale Nantes, 44321 Nantes Cedex 03, France.
authors.forename: Emilie
authors.initials: E
authors.lastname: Chouzenoux
authors.forename: Jérôme
authors.initials: J
authors.lastname: Idier
authors.forename: Saïd
authors.initials: S
authors.lastname: Moussaoui
mesh_terms: D000465:Algorithms; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity