IR Tools: A MATLAB Package of Iterative Regularization Methods and Large-Scale Test Problems

TL;DR

IR Tools is a MATLAB package providing a comprehensive collection of iterative regularization methods and large-scale test problems for linear inverse problems, facilitating research and application in image reconstruction and related fields.

Contribution

The package includes recently proposed iterative methods not available elsewhere and offers flexible implementation options for large-scale inverse problems.

Findings

Includes a wide range of iterative regularization methods.

Provides large-scale test problems for realistic applications.

Demonstrates the effectiveness of various algorithms through numerical examples.

Abstract

This paper describes a new MATLAB software package of iterative regularization methods and test problems for large-scale linear inverse problems. The software package, called IR Tools, serves two related purposes: we provide implementations of a range of iterative solvers, including several recently proposed methods that are not available elsewhere, and we provide a set of large-scale test problems in the form of discretizations of 2D linear inverse problems. The solvers include iterative regularization methods where the regularization is due to the semi-convergence of the iterations, Tikhonov-type formulations where the regularization is explicitly formulated in the form of a regularization term, and methods that can impose bound constraints on the computed solutions. All the iterative methods are implemented in a very flexible fashion that allows the problem's coefficient matrix to be available as a (sparse) matrix, a function handle, or an object. The most basic call to all of the various iterative methods requires only this matrix and the right hand side vector; if the method uses any special stopping criteria, regularization parameters, etc., then default values are set automatically by the code. Moreover, through the use of an optional input structure, the user can also have full control of any of the algorithm parameters. The test problems represent realistic large-scale problems found in image reconstruction and several other applications. Numerical examples illustrate the various algorithms and test problems available in this package.