Removing the impulse noise from images based on fuzzy cellular automata by using a two-phase innovative method

In this paper, a novel method is proposed to restore digital images corrupted by impulse noise. The proposed method in this paper consists of two phases for detecting impulse noise and image restoration. Corrupted pixels in the first phase of the proposed method are identified in two steps. In the first step, the corrupted pixels are identified by minimum values and the average Moore neighborhood pixels of the central pixel. In the second step of this phase, the pixels identified in the previous step as an uncorrupted pixel, are examined whether they are uncorrupted or corrupted once again for further accuracy by a new algorithm. The new algorithm uses cellular automata to identify corrupted pixels by measuring the harmonic and arithmetic means values of four different states of the position of five pixels in the neighborhood of the central pixel. In the second phase of the proposed method, the corrupted pixels are restored using the cosine similarity of the maximization of four different positions of the five pixels neighborhood to the central pixel using fuzzy cellular. The proposed algorithm is evaluated using image enhancement factor (IEF), Structural Similarity Index (SSIM), and Peak Signal to Noise Ratio (PSNR). The proposed method has been tested on 580 images at 15-90% noise density and the qualitative and quantitative results show that the proposed filter is robust enough at different noise levels and meaningful image details such as edges compared to other methods are well maintained.