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EN
In this paper a novel method of noise reduction in color images is presented. The new technique is capable of attenuating both impulsive and Gaussian noise, while preserving and even enhancing sharpness of the image edges. Extensive simulations reveal that the new method outperforms significantly the standard techniques widely used in multivariate signal processing. In this work we apply the new noise reduction method for the enhancement of the images of gene chips. We demonstrate that the new technique is capable of reducing various kinds of noise present in microarray images and that it enables efficient spot location and estimation of the gene expression level due to the smoothing effect and preservation of the spot edges. This paper contains the comparison of the new technique of noise reduction with the standard procedures used for the processing of vector valued images, as well as examples of the efficiency of the new algorithm when applied to typical microarray images.
EN
In this paper a new approach to the problem of impulsive noise reduction for color images is introduced. The presented self-adaptive image filter is based on a model of a virtual particle, which performs a random walk on the image lattice, with transition probabilities derived from the Gibbs distribution. The major advantage of the new filtering technique, is that it filters out the noise component, while adapting itself to the local image structures. In this way the new algorithm is able to eliminate strong impulsive noise, while preserving edges and fine image details. As the algorithm is a fuzzy modification of the commonly used vector median operator, it is very fast and easy to implement. Our results show that the proposed method outperforms all standard algorithms of the reduction of impulsive noise in color images.
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