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.
A Generalized krylov Subspace Method for ℓp-ℓq Minimization / Lanza, A.; Morigi, S.; Reichel, L.; Sgallari, F.. - In: SIAM JOURNAL ON SCIENTIFIC COMPUTING. - ISSN 1064-8275. - STAMPA. - 37:5(2015), pp. 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.File | Dimensione | Formato | |
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