The goal of digital image noise smoothing is to smooth noise in the image without smoothing edges and other high frequency information. Statistically optimal methods must use accurate statistical models of the image and noise. Subjective methods must also characterize the image. Two methods using high frequency information to augment existing noise smoothing methods are investigated: two component model (TCM) smoothing and second derivative enhancement (SDE) smoothing. TCM smoothing applies an optimal noise smoothing filter to a high frequency residual, extracted from the noisy image using a two component source model. The lower variance and increased stationarity of the residual compared to the original image increases this filters effectiveness. SDE smoothing enhances the edges of the low pass filtered noisy image with the second derivative, extracted from the noisy image. Both methods are shown to perform better than the methods they augment, through objective (statistical) and subjective (visual) comparisons.
| Identifer | oai:union.ndltd.org:arizona.edu/oai:arizona.openrepository.com:10150/276599 |
| Date | January 1987 |
| Creators | Jarrett, David Ward, 1963- |
| Contributors | Strickland, Robin |
| Publisher | The University of Arizona. |
| Source Sets | University of Arizona |
| Language | en_US |
| Detected Language | English |
| Type | text, Thesis-Reproduction (electronic) |
| Rights | Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author. |
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