Compressive Hyperspectral Video Detection and Imaging
| dc.contributor.advisor | Kelly, Kevin F. | en_US |
| dc.creator | Lu, Liyang | en_US |
| dc.date.accessioned | 2017-08-02T14:24:01Z | en_US |
| dc.date.available | 2018-05-01T05:01:09Z | en_US |
| dc.date.created | 2017-05 | en_US |
| dc.date.issued | 2017-04-20 | en_US |
| dc.date.submitted | May 2017 | en_US |
| dc.date.updated | 2017-08-02T14:24:01Z | en_US |
| dc.description.abstract | Hyperspectral video imaging remains a challenging task given the high dimensionality of the datasets and the limited imaging spatio-spectral-temporal tradeoffs via current methods. Yet, it has great potential in studying a variety of dynamic optical phenomena, both in microscopic and macroscopic systems. The first part of this thesis describes the design and implementation of spatially compressive hyperspectral imaging for dark-field and broad-band sum-frequency generation microscopy in order to capture and analyze different nanomaterial properties. Next, a compressive classification method using secant patterns is designed to perform task-aware compressive sensing. It achieves fast and efficient classification based on sampling but not full reconstruction using single-pixel camera hardware. Lastly, a novel compressive imaging system, the single-doxel imager (SDI), is demonstrated for four dimensional hyperspectral video imaging. It is uniquely based on a single light modulator and a single detector. By performing optical spatial and spectral modulations simultaneously with a set of designed spatio-spectral modulation patterns, it can encode hyperspectral information into a highly compressed sequence of measurements. Along with the novel optical design, a new compressive imaging reconstruction algorithm is also implemented, which is able to exploit the inherent redundancy in the 4D temporal-spatio-spectral datacube. Using this system, single-pixel hyperspectral video imaging that achieves a compression ratio of 900 to 1 is demonstrated. | en_US |
| dc.embargo.terms | 2018-05-01 | en_US |
| dc.format.mimetype | application/pdf | en_US |
| dc.identifier.citation | Lu, Liyang. "Compressive Hyperspectral Video Detection and Imaging." (2017) Diss., Rice University. <a href="https://hdl.handle.net/1911/96135">https://hdl.handle.net/1911/96135</a>. | en_US |
| dc.identifier.uri | https://hdl.handle.net/1911/96135 | en_US |
| dc.language.iso | eng | en_US |
| dc.rights | Copyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder. | en_US |
| dc.subject | Compressive Imaging | en_US |
| dc.subject | Hyperspectral Imaging | en_US |
| dc.subject | Compressive Classification | en_US |
| dc.subject | Single-Doxel Imager | en_US |
| dc.title | Compressive Hyperspectral Video Detection and Imaging | en_US |
| dc.type | Thesis | en_US |
| dc.type.material | Text | en_US |
| thesis.degree.department | Electrical and Computer Engineering | en_US |
| thesis.degree.discipline | Engineering | en_US |
| thesis.degree.grantor | Rice University | en_US |
| thesis.degree.level | Doctoral | en_US |
| thesis.degree.name | Doctor of Philosophy | en_US |
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