Generalized Selection Weighted Vector Filters
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Generalized Selection Weighted Vector Filters Rastislav Lukac The Edward S. Rogers Sr. Department of Electrical and Computer Engineering, University of Toronto, 10 King’s College Road, Toronto, ON, Canada M5S 3G4 Email: [email protected]
Konstantinos N. Plataniotis The Edward S. Rogers Sr. Department of Electrical and Computer Engineering, University of Toronto, 10 King’s College Road, Toronto, ON, Canada M5S 3G4 Email: [email protected]
Bogdan Smolka Department of Automatic Control, Silesian University of Technology, Akademicka 16, 44-101 Gliwice, Poland Email: [email protected]
Anastasios N. Venetsanopoulos The Edward S. Rogers Sr. Department of Electrical and Computer Engineering, University of Toronto, 10 King’s College Road, Toronto, ON, Canada M5S 3G4 Email: [email protected] Received 21 July 2003; Revised 11 December 2003 This paper introduces a class of nonlinear multichannel filters capable of removing impulsive noise in color images. The hereproposed generalized selection weighted vector filter class constitutes a powerful filtering framework for multichannel signal processing. Previously defined multichannel filters such as vector median filter, basic vector directional filter, directional-distance filter, weighted vector median filters, and weighted vector directional filters are treated from a global viewpoint using the proposed framework. Robust order-statistic concepts and increased degree of freedom in filter design make the proposed method attractive for a variety of applications. Introduced multichannel sigmoidal adaptation of the filter parameters and its modifications allow to accommodate the filter parameters to varying signal and noise statistics. Simulation studies reported in this paper indicate that the proposed filter class is computationally attractive, yields excellent performance, and is able to preserve fine details and color information while efficiently suppressing impulsive noise. This paper is an extended version of the paper by Lukac et al. presented at the 2003 IEEE-EURASIP Workshop on Nonlinear Signal and Image Processing (NSIP ’03) in Grado, Italy. Keywords and phrases: multichannel image processing, color image processing, nonlinear vector filtering, order-statistic theory, adaptive filter design, noise removal.
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INTRODUCTION
Vector signal processing is of paramount importance in application areas such as biomedicine, computer vision, multimedia, robotics, industrial inspection, and remote sensing. In all these areas, end-users and system developers have to work with multidimensional vectorial data sets. The growing interest in the development of vector processing techniques can be attributed primarily to the importance of color image processing [1, 2]. The surge of emerging applications [1] such as web-based processing of color images and videos, enhancement of DNA microarray images, digital archiving and culture heritage preservation, multimedia sequence mining,
and the proliferation of devices [3] such as video-enabled wireless phones and personal
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