Improved adaptive type-2 fuzzy filter with exclusively two fuzzy membership function for filtering salt and pepper noise.

Image denoising is one of the preliminary steps in image processing methods in which the presence of noise can deteriorate image quality. This paper presents an improved two-stage fuzzy filter for filtering salt and pepper noise from the images to enhance the image quality. In the first stage, the pixels in the image are categorized as good or noisy based on adaptive thresholding using type-2 fuzzy logic with exclusively two different membership functions in the filter window. In the second stage, the noisy pixels are denoised using modified ordinary fuzzy logic in the respective filter window. The proposed filter is validated on standard images with various noise levels. The proposed filter removes the noise and preserves beneficial image characteristics, i.e., edges and corners at higher noise levels. The performance of the proposed filter is compared with the various state-of-the-art methods in terms of peak signal-to-noise ratio and computation time. The average PSNR values for the noise percentage 20%, 50% and 80% are the 37%, 31% and 27%. To show the effectiveness of statistical filter tests, i.e., the Friedman test and Bonferroni−Dunn (BD) test are also carried out, which ascertain that the proposed filter outperforms in comparison to various filtering approaches. [ABSTRACT FROM AUTHOR]