This paper presents a new efficient approach for the solution of the ℓp-ℓq minimization problem based on the application of successive orthogonal projections onto generalized Krylov subspaces of increasing dimension. The subspaces are generated according to the iteratively reweighted least-squares strategy for the approximation of ℓp/ℓq-norms by weighted ℓ2-norms. Computed image restoration examples illustrate that it suffices to carry out only a few iterations to achieve highquality restorations. The combination of a low iteration count and a modest storage requirement makes the proposed method attractive.

Lanza, A., Morigi, S., Reichel, L., Sgallari, F. (2015). A Generalized krylov Subspace Method for ℓp-ℓq Minimization. SIAM JOURNAL ON SCIENTIFIC COMPUTING, 37(5), S30-S50 [10.1137/140967982].

A Generalized krylov Subspace Method for ℓp-ℓq Minimization

LANZA, ALESSANDRO;MORIGI, SERENA;REICHEL, LOTHAR;SGALLARI, FIORELLA
2015

Abstract

This paper presents a new efficient approach for the solution of the ℓp-ℓq minimization problem based on the application of successive orthogonal projections onto generalized Krylov subspaces of increasing dimension. The subspaces are generated according to the iteratively reweighted least-squares strategy for the approximation of ℓp/ℓq-norms by weighted ℓ2-norms. Computed image restoration examples illustrate that it suffices to carry out only a few iterations to achieve highquality restorations. The combination of a low iteration count and a modest storage requirement makes the proposed method attractive.
2015
Lanza, A., Morigi, S., Reichel, L., Sgallari, F. (2015). A Generalized krylov Subspace Method for ℓp-ℓq Minimization. SIAM JOURNAL ON SCIENTIFIC COMPUTING, 37(5), S30-S50 [10.1137/140967982].
Lanza, A.; Morigi, S.; Reichel, L.; Sgallari, F.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/521532
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