Date

2014-08-18

Author

Öktem, Sevinç Figen
Davila, Joseph M

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Spectral imaging is a fundamental diagnostic technique for the study of the solar coronal plasma, and spectral data is routinely used to measure the temperature, density, and flow velocity in coronal features. However, obtaining the spectra of a multi-dimensional region with inherently two-dimensional detectors poses intrinsic limitations on the spatio-temporal extent of the technique. In particular, slit spectrographs suffer from a limited instantaneous field-of-view (IFOV), and filter-based spectral imagers offer only limited spectral range or temporal resolution. Here we develop two novel spectral imaging techniques that enable capabilities beyond the reach of these conventional methods. Each development is based on computational imaging, which involves distributing the spectral imaging task between a physical and a computational system, and then digitally forming images of interest from multiplexed measurements by means of solving an inverse problem. In particular, in the first approach, a nonscanning spectral imaging technique is developed to enable performing spectroscopy over a two-dimensional instantaneous field-of-view. This technique combines a parametric estimation approach with a slitless spectrometer configuration. In the second approach, a spectral imaging technique with an optical device known as a photon sieve is developed to achieve superior spatial and spectral resolutions over the conventional filter-based spectral imagers, through wavelength-dependent focusing property of the photon sieve and multiplexed measurements recorded by a photon sieve imaging system with a moving detector. In each of these two techniques, multiplexed measurements are combined with an image-formation model and then the resultant inverse problem is solved computationally for image reconstruction. The associated inverse problems, which can be viewed as multiframe image deblurring problems, are formulated in a Bayesian estimation framework to incorporate additional prior statistical knowledge of the targeted objects. Computationally efficient algorithms are then designed to solve the resulting nonlinear optimization problems. Bayesian Cramer-Rao bounds are obtained to characterize the estimation uncertainties and performance limits. The developed spectral imaging techniques enable finer temporal, spatial, and spectral resolutions, and will facilitate the understanding of highly dynamic solar events such as flares, CMEs, and transient brightening.

URI

http://www.stce.be/sipworkshop2014/abstractbook.php
https://hdl.handle.net/11511/85046

Collections

Department of Electrical and Electronics Engineering, Conference / Seminar