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Detection of pixels corrupted by impulse noise using random point patterns

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Abstract

This paper presents a novel method for the detection of binary- and random-valued impulsive noise in contaminated images. The noise detector has been developed to classify the certain intensity image pixels as corrupted or uncorrupted based on their relative position. To perform such classification, we regard noise as random points and propose to use the properties of random point patterns that are formed based on a noisy image. Pixels of each image intensity are checked by what type of point pattern they form based on the Clark–Evans test. In the case of random or regular type, pixels of this intensity are classified as noise. For intensity pixels designated as a cluster, the search of isolated points is performed. In case of a homogeneity test fail, they are also classified as noise. The proposed technique can be applied to color images. Extensive simulation experiments indicate that the proposed detection approach outperforms many well-known means.

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Kosarevych, R., Lutsyk, O. & Rusyn, B. Detection of pixels corrupted by impulse noise using random point patterns. Vis Comput 38, 3719–3730 (2022). https://doi.org/10.1007/s00371-021-02207-1

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