An efficient approach for limited-data chemical species tomography and its error bounds.
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.
School of Engineering , University of Edinburgh , Edinburgh EH9 3JL, UK.
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Polydorides
School of Engineering , University of Edinburgh , Edinburgh EH9 3JL, UK.
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Tsekenis
School of Engineering , University of Edinburgh , Edinburgh EH9 3JL, UK.
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McCann
Instituto Nacional de Technica Aerospacial , Madrid, Spain.
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Prat
Electrical Engineering , University of Manchester , Manchester M60 1QD, UK.
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Wright
image reconstruction; regularization; tomography