Computational imaging presents opportunities to overcome conventional imaging limits, such as a trade-off between image resolution and system z-height. Optical design and image processing are developed in parallel to optimise imaging system properties for a given application. These properties can be quantified and it is shown that the image quality of a multichanneled imaging system, relying on superresolution (SR) image reconstruction, is dependent on object depth due to interference of sampling phases. A novel multichannel imaging system that does not rely on SR reconstruction, yet achieves a reduction of system height by a factor of two, is presented. The mitigation of SR reconstruction reduces computational effort, offering an attractive option for a computational imaging device in the mobile handset market. The image reconstruction framework required for the reduced height multichanneled imager is proposed in a general form so that it is applicable to many multi-aperture geometries, making it compatible with commercial interests, i.e. it is sensible to develop something with a wide application space. A second novel imaging system is also described in this thesis: A snapshot hyperspectral imaging (HSI) camera, comprising of a square array of miniature camera modules, makes use of the multiple imaging channels to record spectrally distinct images. The generalised image processing framework is applied to the image reconstruction problem to generate the spectral data-cubes. This approach to hyperspectral imaging presents opportunities to gain performance advantages over other snapshot HSI technologies and do so at a significant reduction in cost.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:591956 |
| Date | January 2013 |
| Creators | Downing, James P. D. |
| Publisher | University of Glasgow |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://theses.gla.ac.uk/4825/ |
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