Generalized Row-Action Methods for Tomographic Imaging

Abstract

Row-action methods play an important role in tomographic image reconstruction. Many such methods can be viewed as incremental gradient methods for minimizing a sum of a large number of convex functions, and despite their relatively poor global rate of convergence, these methods often exhibit fast initial convergence which is desirable in applications where a low-accuracy solution is acceptable. In this paper, we propose relaxed variants of a class of incremental proximal gradient methods, and these variants generalize many existing row-action methods for tomographic imaging. Moreover, they allow us to derive new incremental algorithms for tomographic imaging that incorporate different types of prior information via regularization. We demonstrate the efficacy of the approach with some numerical examples.

References in corpus (4)

• NESTA: A Fast and Accurate First-order Method for Sparse Recovery
• Hybrid Deterministic-Stochastic Methods for Data Fitting
• Perturbation Resilience and Superiorization of Iterative Algorithms
• Beneath the valley of the noncommutative arithmetic-geometric mean inequality: conjectures, case-studies, and consequences

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