Membership function

Algorithm

Pixel

Ecology

Biology

Median filter

Gaussian noise

Image processing

Grayscale

Computer science

Fuzzy set

Filter (signal processing)

Adaptive filter

Type (biology)

Pattern recognition (psychology)

Mathematics

Image (mathematics)

Fuzzy logic

Salt-and-pepper noise

Artificial intelligence

Noise reduction

Noise (video)

Computer vision

IEEE Transactions on Fuzzy Systems

This paper proposes a novel adaptive Type-2 fuzzy filter for removing salt and pepper noise from the images. The filter removes noise in two steps. In the first step, the pixels are categorized as good or bad based on their primary membership function (MF) values in the respective filter window. In this paper, two approaches have been proposed for finding threshold between good or bad pixels by designing primary MFs. a) MFs with distinct Means and same Variance and b) MFs with distinct Means and distinct Variances. The primary MFs of the Type-2 fuzzy set is chosen as Gaussian membership functions. Whereas, in the second step, the pixels categorized as bad are denoised. For denoising, a novel Type-1 fuzzy approach based on a weighted mean of good pixels is presented in the paper. The proposed filter is validated for several standard images with the noise level as low as 20% to as high as 99%. The results show that the proposed filter performs better in terms of peak signal-noise-ratio values compared to other state-of-the-art algorithms.

Adaptive Type-2 Fuzzy Approach for Filtering Salt and Pepper Noise in Grayscale Images