Image deblurring with anti-reflective boundary conditions and a non-symmetric point spread function is considered. Several iterative methods based on Krylov subspace projections, as well as Arnoldi-Tikhonov regularization methods, with reblurring right or left preconditioners are compared. The aim of the preconditioner is not to accelerate the convergence, but to improve the quality of the comp...

in this work‎, ‎an iterative method based on a matrix form of lsqr algorithm is constructed for solving the linear operator equation $mathcal{a}(x)=b$‎ ‎and the minimum frobenius norm residual problem $||mathcal{a}(x)-b||_f$‎ ‎where $xin mathcal{s}:={xin textsf{r}^{ntimes n}~|~x=mathcal{g}(x)}$‎, ‎$mathcal{f}$ is the linear operator from $textsf{r}^{ntimes n}$ onto $textsf{r}^{rtimes s}$‎, ‎$ma...

in this paper, we will present a modification of the preconditioned aor-type method for solving the linear system. a theorem is given to show the convergence rate of modification of the preconditioned aor methods that can be enlarged than the convergence aor method.

in this paper, we present a new iterative method with order of convergence eighth for solving nonlinear equations. periteration this method requires three evaluations of the function and one evaluation of its first derivative. a general error analysis providing the eighth order of convergence is given. several numerical examples are given to illustrate the efficiency and performance of the new ...

Given a compact operator G, we consider the ill-posed problem, given y , solve Gφ = y (approximately). Typically, in the presence of noise y / ∈ Range(G). We consider the iterated Tikhonov method for this problem. The method selects a regularization parameter based on stability and corrects several times to increase accuracy. We show that it gives a higher accuracy approximation to the noise-fr...

In a recent paper [24] an algorithm for large-scale Tikhonov regularization in standard form called GKB-FP was proposed and numerically illustrated. In this paper, further insight into the convergence properties of this method is provided and extensions to general-form Tikhonov regularization are introduced. In addition, as alternative to Tikhonov regularization, a preconditioned LSQR method co...

We develop a homotopy method for nonlinear inverse problems, where the forward problems are governed by some forms of differential equations. A Tikhonov-style regularization approach yields an optimization problem. Ordinary iterative methods may fail to solve this problem, due to their locally convergent properties. Then the fixed-point homotopy method is introduced to solving the normal equati...

Tikhonov regularization is one of the most popular methods for solving linear systems of equations or linear least-squares problems with a severely ill-conditioned matrix A. This method replaces the given problem by a penalized least-squares problem. The present paper discusses measuring the residual error (discrepancy) in Tikhonov regularization with a seminorm that uses a fractional power of ...

The reconstruction methods for solving the ill-posed inverse problem of photoacoustic tomography with limited noisy data are iterative in nature to provide accurate solutions. These performance is highly affected by noise level data. A singular value decomposition (SVD) based plug and play priors method was proposed this work robustness shown be superior as compared total variation regularizati...

In this paper we present and study a new class of regularized kernel methods for learning vector fields, which are based on filtering the spectrum of the kernel matrix. These methods include Tikhonov regularization as a special case, as well as interesting alternatives such as vector valued extensions of L2-Boosting. Our theoretical and experimental analysis shows that spectral filters that yie...