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      New transform to project axisymmetric deflection fields along arbitrary rays

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      Measurement Science and Technology
      IOP Publishing

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          Abstract

          Axisymmetric tomography is used to extract quantitative information from line-of-sight measurements of gas flow and combustion fields. For instance, background-oriented schlieren (BOS) measurements are typically inverted by tomographic reconstruction to estimate the density field of a high-speed or high-temperature flow. Conventional reconstruction algorithms are based on the inverse Abel transform, which assumes that rays are parallel throughout the target object. However, camera rays are not parallel, and this discrepancy can result in significant errors in many practical imaging scenarios. We present a generalization of the Abel transform for use in tomographic reconstruction of light-ray deflections through an axisymmetric target. The new transform models the exact path of camera rays instead of assuming parallel paths, thereby improving the accuracy of estimates. We demonstrate our approach with a simulated BOS scenario in which we reconstruct noisy synthetic deflection data across a range of camera positions. Results are compared to state-of-the-art Abel-based algorithms. Reconstructions computed using the new transform are consistently more stable and accurate than conventional reconstructions.

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          On the Implementation of a Primal-Dual Interior Point Method

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            Practical Poissonian-Gaussian noise modeling and fitting for single-image raw-data.

            We present a simple and usable noise model for the raw-data of digital imaging sensors. This signal-dependent noise model, which gives the pointwise standard-deviation of the noise as a function of the expectation of the pixel raw-data output, is composed of a Poissonian part, modeling the photon sensing, and Gaussian part, for the remaining stationary disturbances in the output data. We further explicitly take into account the clipping of the data (over- and under-exposure), faithfully reproducing the nonlinear response of the sensor. We propose an algorithm for the fully automatic estimation of the model parameters given a single noisy image. Experiments with synthetic images and with real raw-data from various sensors prove the practical applicability of the method and the accuracy of the proposed model.
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              Inverse problems with structural prior information

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                Author and article information

                Contributors
                Journal
                Measurement Science and Technology
                Meas. Sci. Technol.
                IOP Publishing
                0957-0233
                1361-6501
                December 21 2021
                March 01 2022
                December 21 2021
                March 01 2022
                : 33
                : 3
                : 035201
                Article
                10.1088/1361-6501/ac3f83
                038c5382-cad1-42d2-b2d8-b87c07130d5b
                © 2022

                https://iopscience.iop.org/page/copyright

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