Impulsive and Poisson Noises Removal Using Takagi Neuro-Fuzzy Network

(Sarah Behnam Aziz and Abu Hail Maitham martyr)

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Abstract

A new Takagi neuro-fuzzy filter is presented for the noise reduction of images corrupted with impulsive and Poisson noises. In the current paper, the number of neuro-fuzzy connections is reduced to be equal to the number of membership functions. Also, the time of computation is reduced by using artificial image for training the presented neuro-fuzzy filter. The filter can be applied effectively to reduce heavy noise. Experimental results are obtained to show the feasibility of the proposed approach. These results are good when compared to other filters by numerical measures and visual inspection. The presented scheme is applied to grayscale and truecolor images. The presented scheme is efficient, fast, and can be extended by adding other filter as input, without smoothing an image. Keywords: Impulsive noise; Poisson noise; Median filter; Average filter; Takagi Neuro-Fuzzy
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