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Color image denoising using evolutionary computation
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DOI：10.1002/ima.20058.png)
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Abstract
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Noise suppression in multichannel data sets, such as color images, has drawn much attention in the last few years. An issue of paramount importance in designing color image filters is the determination of the coefficients that should be used to weight the inputs to the filter. In this study, we propose an evolutionary computation-based approach to select and optimize the coefficients in the class of weighted vector directional filters. Using a genetic algorithm, we were able to adapt the filter weights to match varying image and noise characteristics. Extended experimentation with realistic image processing applications, including television image enhancement and virtual restoration of artworks, indicates that the proposed filters are capable of removing noise while preserving chromaticity information, edges, and fine details, as well as structural image content. (C) 2006 Wiley Periodicals, Inc.
Keywords:
color image filtering
vector directional processing
genetic algorithm
constrained optimization
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