Two new efficient iterative regularization methods for image restoration problems
Summary: Iterative regularization methods are efficient regularization tools for image restoration problems. The \(\mathrm{IDR}(s)\) and LSMR methods are state-of-the-arts iterative methods for solving large linear systems. Recently, they have attracted considerable attention. Little is known of them as iterative regularization methods for image restoration. In this paper, we study the regularization properties of the \(\mathrm{IDR}(s)\) and LSMR methods for image restoration problems. Comparative numerical experiments show that \(\mathrm{IDR}(s)\) can give a satisfactory solution with much less computational cost in some situations than the classic method LSQR when the discrepancy principle is used as a stopping criterion. Compared to LSQR, LSMR usually produces a more accurate solution by using the \(L\)-curve method to choose the regularization parameter.
scientific article; zbMATH DE number 6306043
scientific article