This thesis presents a new technique for performing image analysis, synthesis, and modification using a generalized Gaussian model. The joint time-frequency characteristics of a generalized Gaussian are combined with the flexibility of the analysis-by-synthesis (ABS) decomposition technique to form the basis of the model. The good localization properties of the
Gaussian make it an appealing basis function for image analysis, while the ABS process provides a more flexible representation with enhanced functionality. ABS was first explored in conjunction with sinusoidal modeling of speech and audio signals [George87]. A 2D extension of the ABS technique is developed here to perform the image decomposition. This model forms the basis for new approaches in image analysis
and enhancement.
The major contribution is made in the resolution enhancement of images generated using coherent imaging modalities such as Synthetic Aperture Radar (SAR) and ultrasound. The ABS generalized Gaussian model is used to decouple natural image
features from the speckle and facilitate independent control over feature characteristics and speckle granularity. This has the beneficial effect of increasing the perceived resolution and
reducing the obtrusiveness of the speckle while preserving the edges and the definition of the image features. A consequence of its inherent flexibility, the model does not preclude image
processing applications for non-coherent image data. This is illustrated by its application as a feature extraction tool for a FLIR imagery complexity measure.
| Identifer | oai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/14054 |
| Date | 16 November 2006 |
| Creators | Britton, Douglas Frank |
| Publisher | Georgia Institute of Technology |
| Source Sets | Georgia Tech Electronic Thesis and Dissertation Archive |
| Language | en_US |
| Detected Language | English |
| Type | Dissertation |
| Format | 11627064 bytes, application/pdf |
Page generated in 0.0018 seconds