Salt and pepper noise removal from a digital image using a new approach based on fuzzy cellular automata and Anfis

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Piroozmandan Mohammad Mehdi; Farokhi Fardad; Piroozmandan Mohammad Ali

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Journal Paper

Paper Information

Journal: International Journal of Smart Electrical Engineering Year:2024 | Volume:13 | Issue:2 Page(s): 147-155

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Information Journal Paper

Title

Salt and pepper noise removal from a digital image using a new approach based on fuzzy cellular automata and Anfis

Author(s)

Piroozmandan Mohammad Mehdi | Farokhi Fardad | Piroozmandan Mohammad Ali | Issue Writer Certificate

Keywords

Cellular AutomataQ4

Fuzzy Cellular AutomataQ4

AnfisQ4

noise detectionQ4

de-noisingQ4

Abstract

This paper proposes a novel method for restoring images corrupted by impulse noise. This new method is based on fuzzy Cellular Automata and an adaptive neural fuzzy inference system (Anfis). The proposed method consists of two phases: identifying and removing salt and pepper noise. In the first phase of the method proposed, salt and pepper noise pixels are identified in two steps. In the first step of the first phase, salt and pepper noises are detected by the average and minimum values of the pixels in the neighborhood of the center pixel. In order to improve the accurate rate of noise detection, pixels that are not detected as noise are re-evaluated by a new algorithm in the second step of the first phase. This new algorithm uses the measure of cosine similarity of Moore's neighborhood values around the central cell, which is based on four types of pixel placement patterns. The state of the pixels is re-evaluated by the fuzzy Cellular Automata. In the second phase of the proposed method, noisy pixels are restored using Anfis based on Moore neighborhood pixels around the central cell. The method proposed in this paper is evaluated using PSNR and SSIM. Also, the quantitative and qualitative results show that the new method proposed in this paper is robust in different noise levels from 10% to 90%, and image details such as edges are preserved better compared to other filters. .

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