Tunable Diode Laser Absorption Spectroscopy (TDLAS) is an emerging technique for simultaneous sensing of temperature and concentration of gaseous media. However, simultaneous reconstruction of temperature and concentration using TDLAS measurements is a nonlinear inverse problem and unlike other forms of computed tomography (CT), it is typically not possible to take a large number of projection measurements; so reconstructions are often computed using simplistic assumptions that limit the usability of the results.In this paper, we present a fast algorithm for model-based iterative reconstruction (MBIR) of TDLAS data. Our TDLAS-MBIR method uses a nonlinear forward model based on the physics of light absorption and incorporates a holistic prior model that can be learned from very sparse training data. Reconstructions performed on computational fluid dynamics (CFD) phantoms show that our proposed reconstruction algorithm is fast; works well when the number of pixels, p, far exceeds the number of measurements, M ; is robust against noise; and produces good reconstructions using few training examples for the prior model.
Tunable diode laser absorption tomography (TD-LAT) has emerged as a popular non-intrusive technique for simultaneous sensing of gas concentration and temperature. Major challenges of TDLAT include availability of limited projection measurements and limited training data. Conventional tomographic techniques are therefore not directly applicable. Usually approximations are made which are limited in scope. In this paper, we propose a novel model-based iterative reconstruction (MBIR) framework for TDLAT imaging of gas concentration and temperature. First, we propose a novel prior model that captures non-homogeneous and non-Gaussian characteristics of the images by modeling their distribution as a Gaussian mixture and impose constraints on the mixture parameters to avoid overfitting of the sparse training set. Next, we present the nonlinear forward model of TDLAT. We formulate the inversion problem into a MAP estimation problem and propose a multigrid optimization algorithm that solves the resulting optimization problem in eigenimage basis using surrogate functions for the non-convex prior. We demonstrate the efficacy of our approach by performing reconstructions of simulated TDLAT data.
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