Majorization–minimization generalized Krylov subspace methods for $${\ell _p}$$–$${\ell _q}$$ℓq optimization applied to image restoration

摘要: A new majorization–minimization framework for \(\ell _p\)–\(\ell _q\) image restoration is presented. The solution sought in a generalized Krylov subspace that build up during the process. Proof of convergence to stationary point minimized functional provided both convex and nonconvex problems. Computed examples illustrate high-quality restorations can be determined with modest number iterations storage requirement method not very large. comparison related methods shows competitiveness proposed.