  Image denoising is one of the preliminary steps in image processing methods
in which the presence of noise can deteriorate the image quality. To overcome
this limitation, in this paper a improved two-stage fuzzy filter is proposed
for filtering salt and pepper noise from the images. 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 useful image
characteristics, i.e., edges and corners at higher noise level. 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. To show the
effectiveness of filter statistical tests, i.e., Friedman test and
Bonferroni-Dunn (BD) test are also carried out which clearly ascertain that the
proposed filter outperforms in comparison of various filtering approaches.
