KEYWORDS: Nonlinear filtering, Image filtering, Digital filtering, Speckle, Image processing, Medical imaging, Linear filtering, Electronic filtering, Image segmentation, Signal processing
In coherent image acquisition systems, occurrence of speckle noise is a common phenomenon that is hard to remove without degrading the original image. Conventional linear filtering processes fail to remove this noise and improve the signal to noise ratio without degrading the image. Some statistical filtering processes like the Wiener filter and the Local Linear Minimum Mean Squared Estimator (LLMMSE) filter have been applied with limited success. Nonlinear filtering processes such as the morphological filters are able to reduce some of this inherently associated noise with less degradation. A new approach to non-linear filtering that is capable of removing speckle noise without noticeable degradation is presented in this paper. This approach is based on an adaptive fuzzy leader clustering network known as AFLC. AFLC is a neuro-fuzzy clustering algorithm that can be used to cluster noise pixels in the image separately. After the clustering process is completed, a search is performed throughout the image to localize the noise pixels and to eliminate them using a spatial technique similar to the median filter. The results achieved by this process have been compared with the results from the traditional median filter, the LLMMSE filter, and the connectivity-preserving morphological filter demonstrating superior performance of AFLC in removing speckle noise.
Pattern recognition by fuzzy, neural, and neuro-fuzzy approaches, has gained popularity partly because of intelligent decision processes involved in some of the above techniques, thus providing better classification and partly because of simplicity in computation required by these methods as opposed to traditional statistical approaches for complex data structures. However, the accuracy of pattern classification by various methods is often not considered. This paper considers the performance of major fuzzy, neural, and neuro-fuzzy pattern recognition algorithms and compares their performances with common statistical methods for the same data sets. For the specific data sets chosen namely the Iris data set, an the small Soybean data set, two neuro-fuzzy algorithms, AFLC and IAFC, outperform other well- known fuzzy, neural, and neuro-fuzzy algorithms in minimizing the classification error and equal the performance of the Bayesian classification. AFLC, and IAFC also demonstrate excellent learning vector quantization capability in generating optimal code books for coding and decoding of large color images at very low bit rates with exceptionally high visual fidelity.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.