Noise and Degradation Reduction for Signal and Image Processing via Non-adaptive Convolution Filtering
|dc.contributor.author||Bjerke, Benjamin A.||en_US|
|dc.description.abstract||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.
|dc.rights||This Item is protected by copyright and/or related rights. Some uses of this Item may be deemed fair and permitted by law even without permission from the rights holder(s), or the rights holder(s) may have licensed the work for use under certain conditions. For other uses you need to obtain permission from the rights holder(s).||en_US|
|dc.subject||Synthetic Aperture Radar||en_US|
|dc.title||Noise and Degradation Reduction for Signal and Image Processing via Non-adaptive Convolution Filtering||en_US|
|dc.description.degree||Master of Science||en_US|
|thesis.degree.name||Master of Science||en_US|
|thesis.degree.grantor||Virginia Polytechnic Institute and State University||en_US|
|dc.contributor.committeechair||Roan, Michael J.||en_US|
|dc.contributor.committeemember||Papenfuss, Cory M.||en_US|
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