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Least mean square algorithm tuned by fuzzy c-mean for impulsive noise suppression of gray-level images

In this paper, a new median filter switcher is presented for suppression of impulsive noise in gray-level images. The proposed filter is Modified Adaptive Center Weighted Median (MACWM) filter with an adjustable central weight obtained by partitioning the observation vector space. Dominant points of the proposed approach are partitioning of observation vector space using fuzzy c-means clustering method, training procedure using LMS algorithm and then applying the freezing weights of each block to test image. The exprimental results show better performance in the impulse noise reduction over standard images relative the median (MED) filter, the switching scheme I (SWM-I) filter, the signal dependent rank order mean (SD-ROM) filter, the tristate median (TSM) filter, the fast peer group filter (FPGF), the fuzzy median (FM) filter, the PFM filter and the adaptive center weighted median (ACWM) filter.