A Generalized krylov Subspace Method for \u2113p-\u2113q Minimization

This paper presents a new efficient approach for the solution of the \u2113p-\u2113q 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 \u2113p/\u2113q-norms by weighted \u21132-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