A Type-2 Fuzzy Systems Approach for Clustering-Based Identification of a T-S Regression Model

Bharadhwaj, Homanga; Singh, Vikas; Verma, Nishchal K.

doi:10.1007/978-981-13-1132-1_28

Cited by 4 publications

(1 citation statement)

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“…To overcome the limitations of ordinary fuzzy set, a type-2 fuzzy set was proposed by Zadeh [20]. The type-2 fuzzy set has primary membership function and corresponding to each primary membership function there is a secondary membership function (MF) [21], [22]. Liang and Mendel have presented an adaptive fuzzy filter using type-2 Takagi-Sugeno-Kang (TSK) fuzzy model for equalization of non-linear time varying channels [23].…”

Section: Introductionmentioning

confidence: 99%

Improved Adaptive Type-2 Fuzzy Filter with Exclusively Two Fuzzy Membership Function for Filtering Salt and Pepper Noise

Singh¹,

Agrawal²,

Sharma³

et al. 2020

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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.

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Section: Introductionmentioning

confidence: 99%