Abstract

Driven by the advances in signal processing and ubiquitous availability of high-speed low-cost computing resources over the past decade, computational imaging has seen the growing interest. Improvements on spatial, temporal, and spectral resolutions have been made with novel designs of imaging systems and optimization methods. However, there are two limitations in computational imaging. 1), Computational imaging requires full knowledge and representation of the imaging system called the forward model to reconstruct the object of interest. This limits the applications in the systems with a parameterized unknown forward model such as range imaging systems. 2), the regularization in the optimization process incorporates strong assumptions which may not accurately reflect the a priori distribution of the object. To overcome these limitations, we propose 1) novel optimization frameworks for applying computational imaging on active and passive range imaging systems and achieve 5-10 folds improvement on temporal resolution in various range imaging systems; 2) a data-driven method for estimating the distribution of high dimensional objects and a framework of adaptive sensing for maximum information gain. The adaptive strategy with our proposed method outperforms Gaussian process-based method consistently. The work would potentially benefit high-speed 3D imaging applications such as autonomous driving and adaptive sensing applications such as low-dose adaptive computed tomography(CT).

Notes

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Graduation Date

2019

Semester

Fall

Advisor

Pang, Sean

Degree

Doctor of Philosophy (Ph.D.)

College

College of Optics and Photonics

Department

Optics and Photonics

Degree Program

Optics and Photonics

Format

application/pdf

Identifier

CFE0007867

URL

http://purl.fcla.edu/fcla/etd/CFE0007867

Language

English

Release Date

December 2019

Length of Campus-only Access

None

Access Status

Doctoral Dissertation (Open Access)

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