Optical tomography is an imaging modality that explores the distribution of optical parameters in tissues. In this paper, the regularization jointing both T V and L 1 norm is studied for absorption parameter identification based on radiative transport equation. The T V + L 1 framework is introduced containing L 2 data fidelity, T V regularizer and L 1 regularizer. We demonstrate the existence, stability and convergence of the minima with respect to this T V + L 1 regularization. A novel algorithm for solving related optimization problem is proposed based on reweighted method and technique of split-Bregman. Simulations are performed to show that the proposed reweighted T V + L 1 regularization is more capable of preserving geometric structure of inclusions, quantifying values of absorption parameter and promoting fast convergence compared with T V or L 1 regularization, and is potential for breast cancer imaging. Moreover, it is robust to noise.
Journal of Computational and Applied Mathematics – Elsevier
Published: Aug 1, 2018
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