In this chapter, the effect of transform features (shift sensitivity and directional selectivity) has been examined by implementing the homomorphic and non-homomorphic speckle suppressors in three alternative wavelet domains-DWT, RDWT and CWT. The experimental results demonstrate that performance of DWT is worst on all type of images whereas the performance of RDWT and DT-CWT is comparable. On certain types of images (images dominated by uniform areas), RDWT performs better while on others (textured images), DT-CWT-based methods are the best. This shows that the shift-dependence of a transform causes a significant degradation in performance of a despeckling technique and good directional selectivity is essential for representing the textured images optimally. From these investigations, it is concluded that both DT-CWT and RDWT are equally good for designing wavelet-based de-noising applications. However, the low computational complexity of DT-CWT and the textured nature of medical US images, favours the use of DT-CWT in comparison to RDWT for despeckling applications. More work is required to develop shorter length filters for the DTCWT (e.g. analogous to Haar wavelet) in order to further improve the despeckling performance of the US images.
Wavelet despeckle filtering, Page 1 of 2
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