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oai:www.ideals.illinois.edu:2142/50384 2023-07-11 col_2142_5131 col_2142_8888 com_2142_5130 com_2142_8887 com_2142_234

Kamalabadi, Farzad Kamalabadi, Farzad Blahut, Richard E. Bresler, Yoram Davila, Joseph M. Oktem, Sevinc 2014-09-16T17:12:26Z 2014-09-16T17:12:26Z 2016-09-22T20:59:27Z 2014-08 2014-09-16 2014-08 In this thesis, we develop a class of novel spectral imaging techniques that enable capabilities beyond the reach of 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 relative to conventional filter-based spectral imagers. This technique relies on the 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 the additional prior statistical knowledge of the targeted objects. Computationally efficient algorithms are then designed to solve the resulting nonlinear optimization problems. In addition to the development of each technique, Bayesian Cramer-Rao bounds are also obtained to characterize the estimation uncertainties and performance limits, as well as to explore the optimized system design. The effectiveness of the spectral imaging techniques are illustrated for an application in remote sensing of the solar atmosphere. Lastly, the phase retrieval problem, another inverse problem that arises in the photon-sieve imaging setting with coherent illumination, is studied to devise computationally efficient algorithms. As a whole, the developed spectral imaging techniques enable finer spectral information in the form of higher temporal, spatial, and spectral resolutions. This will enhance the unique diagnostic capabilities of conventional spectral imaging systems in applications as diverse as physics, chemistry, biology, medicine, astronomy and remote sensing. Item withdrawn by Mark Zulauf (zulauf@illinois.edu) on 2014-07-15T13:54:19Z Item was in collections: University of Illinois Theses & Dissertations (ID: 1) No. of bitstreams: 2 Oktem_Sevinc.tex: 707169 bytes, checksum: e07837153bf88c1f9ece6f94a61dcaee (MD5) Oktem_Sevinc.pdf: 7454485 bytes, checksum: 3853fce9fe3155c9660490db22942140 (MD5) Made available in DSpace on 2014-09-16T17:12:26Z (GMT). No. of bitstreams: 3 Sevinc_Oktem.pdf: 7517870 bytes, checksum: 9f62614eaf2a7a39f8b6d5b4186e2d98 (MD5) Oktem_Sevinc.tex: 707169 bytes, checksum: e07837153bf88c1f9ece6f94a61dcaee (MD5) license.txt: 4060 bytes, checksum: bc53f46b83d44a265d350eb56c4c5564 (MD5) Embargo set by: Seth Robbins for item 50495 Lift date: 2016-09-16T17:13:01Z Reason: Author requested closed access (OA after 2yrs) in Vireo ETD system Limited Restriction Lifted for Item 50495 on 2016-09-22T20:59:27Z. http://hdl.handle.net/2142/50384 en Copyright 2014 Sevinc Oktem. Portions of this dissertation have been published elsewhere: IEEE copyrighted papers: 1) F. S. Oktem, J. M. Davila, and F. Kamalabadi, “Image formation model for photon sieves,” in IEEE Int. Conf. on Image Processing (ICIP), 2013, pp. 2373–2377. 2) F. S. Oktem, F. Kamalabadi, and J. M. Davila, ``Cramer-Rao bounds and instrument optimization for slitless spectroscopy,'' in IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP), 2013, pp. 2169-2173. spectral imaging imaging spectroscopy computational imaging inverse methods maximum posterior estimation Bayesian Cramer-Rao bounds multiframe image deblurring parameter estimation of superimposed signals Phase retrieval dynamic programming instantaneous (non-scanning) spectral imaging slitless spectrometer photon sieve image formation diffractive imaging space remote sensing solar spectral imaging Computational imaging and inverse techniques for high-resolution and instantaneous spectral imaging text Electrical & Computer Eng 1933 Electrical & Computer Engr 1200 University of Illinois at Urbana-Champaign Dissertation Ph.D. PHD:Electr & Computer Eng-UIUC 10KS1200PHD