Hybrid and iteratively reweighted regularization by unbiased predictive risk and weighted GCV for projected systems

TL;DR

This paper develops and analyzes regularization techniques for large-scale ill-posed problems, using iterative methods and parameter estimation strategies to improve solution quality in image reconstruction and related applications.

Contribution

It introduces a hybrid regularization approach with unbiased predictive risk and weighted GCV for projected systems, including an iteratively reweighted method for edge preservation.

Findings

Unbiased predictive risk estimator effectively guides regularization parameter selection.

Weighted GCV improves regularization in severely ill-posed problems.

Numerical results confirm the methods' effectiveness in image reconstruction tasks.

Abstract

Tikhonov regularization for projected solutions of large-scale ill-posed problems is considered. The Golub-Kahan iterative bidiagonalization is used to project the problem onto a subspace and regularization then applied to find a subspace approximation to the full problem. Determination of the regularization parameter for the projected problem by unbiased predictive risk estimation, generalized cross validation and discrepancy principle techniques is investigated. It is shown that the regularized parameter obtained by the unbiased predictive risk estimator can provide a good estimate for that to be used for a full problem which is moderately to severely ill-posed. A similar analysis provides the weight parameter for the weighted generalized cross validation such that the approach is also useful in these cases, and also explains why the generalized cross validation without weighting is not always useful. All results are independent of whether systems are over or underdetermined. Numerical simulations for standard one dimensional test problems and two dimensional data, for both image restoration and tomographic image reconstruction, support the analysis and validate the techniques. The size of the projected problem is found using an extension of a noise revealing function for the projected problem Hn\u etynkov\'a, Ple\u singer, and Strako\u s, [\textit{BIT Numerical Mathematics} {\bf 49} (2009), 4 pp. 669-696.]. Furthermore, an iteratively reweighted regularization approach for edge preserving regularization is extended for projected systems, providing stabilization of the solutions of the projected systems and reducing dependence on the determination of the size of the projected subspace.