High-density impulse noise removal using fuzzy mathematical morphology

Abstract

This paper proposes a filtering method for highdensity impulse noise removal based on the fuzzy mathematical morphology using t-norms. The method is a two phased method. In the first phase, an impulse noise detector based on the fuzzy tophat transforms is used to identify pixels which are likely to be contaminated by noise. In the second phase, the image is restored using a specialized regularization method using fuzzy open-close or fuzzy close-open sequences applied only to those selected contaminated pixels and applying then a block smart erase algorithm. Experimental results show that the proposed algorithm presents a better performance in terms of edge preservation and noise suppression than other nonlinear filtering methods, including the presented in [1], in which this method is based on.

Impulsive Noise Removal with an Adaptive Weighted Arithmetic Mean Operator for Any Noise Density

“…These methods generalise the binary morphology (see [9]) using fuzzy sets theory (we refer the reader to [10,11]). For example, in [12], an improvement of the method introduced in [2] is presented using a fuzzy mathematical morphology approach. An even better improvement of the previous filter is proposed in [13] by using another function to identify noisy pixels and a window with an adaptive size.…”

Impulsive noise in images: a brief review

“…In 2013 González-Hidalgo et al implemented a fuzzy mathematical morphology technique where detection is done by fuzzy top-hat transforms & reduction by T-norm [12] and mathematical morphing produce better result than that of DBA, SMF3, SMF5, AMF9, AMF17 [12]. Narayanan et al [10] implemented trimmed median filter (TMF) [10] which is iteration based adoptive trimmed median filtering along with adoptive window trimmed filtering.…”

“…Non-linear filtering [2,12,13] is more complicated but effective filtering methodology, which consist of two phases. In first phase detection of noisy pixel is take place and then in second phase reduction technique is implemented over the noisy pixel.…”