Noise and degradation reduction is of significant importance in virtually all systems where these phenomena are present, specifically in the fields of signal and image processing. The effect of image processing on target detection is of significant interest because noise and degradations can greatly reduce the effectiveness of detection algorithms, due to the presence of high intensity noise which is often mistaken as a target. In signal processing, noise in vibration data, or any time-series data, can reduce the accuracy of measurement and can prevent the passing of useful information.
Many filters that have been developed are designed to reduce a single class of noise, such as Wiener and Frost filters. When these filters are applied to types of noise that they were not designed for, the effect of the noise reduction can be greatly reduced. The proposed Two-Stage Non-Adaptive Convolution (TSNAC) filter significantly reduces both additive and multiplicative noise in these two unique systems.
The performance of these filters is compared through several Image Quality (IQ) metrics. It will be shown that the proposed TSNAC filter reduces noise and degradations more effectively in both SAR images and synthetic vibration data than the competing filters. It will show higher IQ scores, greater computational efficiency in target detection, and significant improvement in signal restoration of simulated vibration data. / Master of Science
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/23700 |
| Date | 13 August 2013 |
| Creators | Bjerke, Benjamin A. |
| Contributors | Mechanical Engineering, Roan, Michael J., Leonessa, Alexander, Papenfuss, Cory M. |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Detected Language | English |
| Type | Thesis |
| Format | ETD, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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