A Smoothing Descent Method for Nonconvex TV-Models
نویسندگان
چکیده
A novel class of variational models with nonconvex lq-normtype regularizations (0 < q < 1) is considered, which typically outperforms those models with convex regularizations in restoring sparse images. Due to the fact that the objective function is nonconvex and non-Lipschitz, such nonconvex models are very challenging from an analytical as well as numerical point of view. In this work we propose a smoothing descent method with provable convergence properties. Numerical experiments are also reported to illustrate the effectiveness of our method.
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تاریخ انتشار 2012