Plenary speaker [Digital image restoration operators based on neuro-fuzzy techniques]

Summary form only given. In this tutorial, we will begin by a quick review of the fundamental concepts of fuzzy and neurofuzzy systems as well as their application to digital image data. Then, we will derive a generalized neuro-fuzzy (NF) based operator suitable for a range of different applications in image processing. Specifically, we will consider three different applications of the presented NF operator: (1) noise filter, (2) noise detector and (3) edge extractor. In the noise filter application, the NF operator will be employed as a detail-preserving noise filtering operator to restore digital images corrupted by impulse noise without degrading fine details and texture in the image. In the noise detector application, the NF operator will be employed as an intelligent decision maker and utilized to detect impulses in images corrupted by impulse noise. Hence, the NF operator will be used to guide a noise filter so that the filter will restore only the pixels that are detected by the NF operator as impulses, and leave the other pixels (i.e. the uncorrupted pixels) unchanged. Consequently, the NF operator will help reduce the undesirable distortion effects of the noise filter. In the edge extractor application, the NF operator will be used to extract edges from digital images corrupted by impulse noise without needing a pre-filtering of the image by an impulse noise filter.