We present a total-variation-based image reconstruction algorithm for electrical capacitance tomography. This is a nonlinear iterative algorithm designed to minimize both the data error and the total variation of the permittivity, with iterations updated by the projected Gauss–Newton steps. We present numerical examples to illustrate the effectiveness of the algorithm in reconstructing permittivity images from both noise-free and noisy capacitance data.
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Weifu Fang (2004) studied this question.
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