Publication date
January 1973
DOI ISSN
0302-9743 Citation count (estimate)
1Abstract
A novel class of variational models with nonconvex q -norm-type regularizations ( 0<q<1 ) is considered, which typically outperforms popular models with convex regularizations in restoring sparse images. Due to the fact that the objective function is nonconvex and non-Lipschitz, such models are very challenging from an analytical as well as numerical point of view. In this work a smoothing descent method with provable convergence properties is proposed for computing stationary points of the underlying variational problem. Numerical experiments are reported to illustrate the effectiveness of the new method
Add to ORKG