Majorization–minimization generalized Krylov subspace methods for – optimization applied to image restoration
BIT Numerical Mathematics, 2017•Springer
A new majorization–minimization framework for ℓ _p ℓ p–ℓ _q ℓ q image restoration is
presented. The solution is sought in a generalized Krylov subspace that is build up during
the solution process. Proof of convergence to a stationary point of the minimized ℓ _p ℓ p–ℓ
_q ℓ q functional is provided for both convex and nonconvex problems. Computed examples
illustrate that high-quality restorations can be determined with a modest number of iterations
and that the storage requirement of the method is not very large. A comparison with related …
presented. The solution is sought in a generalized Krylov subspace that is build up during
the solution process. Proof of convergence to a stationary point of the minimized ℓ _p ℓ p–ℓ
_q ℓ q functional is provided for both convex and nonconvex problems. Computed examples
illustrate that high-quality restorations can be determined with a modest number of iterations
and that the storage requirement of the method is not very large. A comparison with related …
Abstract
A new majorization–minimization framework for – image restoration is presented. The solution is sought in a generalized Krylov subspace that is build up during the solution process. Proof of convergence to a stationary point of the minimized – functional is provided for both convex and nonconvex problems. Computed examples illustrate that high-quality restorations can be determined with a modest number of iterations and that the storage requirement of the method is not very large. A comparison with related methods shows the competitiveness of the method proposed.
Springer
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