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Split Bregman iterative algorithm for sparse reconstruction of electrical impedance tomography

Authors
Journal
Signal Processing
0165-1684
Publisher
Elsevier
Volume
92
Issue
12
Identifiers
DOI: 10.1016/j.sigpro.2012.05.027
Keywords
  • Electrical Impedance Tomography (Eit)
  • L1-Norm Regularized Reconstruction
  • Split Bregman Iterations
Disciplines
  • Computer Science
  • Medicine

Abstract

Abstract In this paper, we present an evaluation of the use of split Bregman iterative algorithm for the L1-norm regularized inverse problem of electrical impedance tomography. Simulations are performed to validate that our algorithm is competitive in terms of the imaging quality and computational speed in comparison with several state-of-the-art algorithms. Results also indicate that in contrast to the conventional L2-norm regularization method and total variation (TV) regularization method, the L1-norm regularization method can sharpen the edges and is more robust against data noises.

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