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Removal of High Density Impulse Noise Using Cloud Model Filter

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Executive Summary

The fact that makes image denoising a difficult task is uncertainties in the impulse noise. The most knowledge in dayflies is uncertainty and erratic, unfortunately it is similar to impulse noise. The mathematic implements for handling uncertainty mostly are probability theory and fuzzy mathematics. That means, among the uncertainties involved in impulse noise, the randomness and the fuzziness are the two most important features. In this paper, the authors use a detail-preserving filter based on the Cloud Model (CM) to remove severe impulse noise. CM is an uncertain conversion model, between qualitative and quantitative description that integrates the concept of randomness and fuzziness. The normal random number generation method in normal cloud generator algorithm overcomes the insufficiency of common method to generate random numbers.

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