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We present a computationally efficient reconstruction method for the limited-data chemical species tomography problem that incorporates projection of the unknown gas concentration function onto a low-dimensional subspace, and regularization using prior information obtained from a simple flow model. In this context, the contribution of this work is on the analysis of the projection-induced data errors and the calculation of bounds for the overall image error incorporating the impact of projection and regularization errors as well as measurement noise. As an extension to this methodology, we present a variant algorithm that preserves the positivity of the concentration image.
|Number of pages||17|
|Journal||Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences|
|Early online date||1 Mar 2016|
|Publication status||Published - 31 Mar 2016|
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