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Top1. Introduction
The sensor malfunctions at some stage in image acquisition, hardware failures in the course of storage of image, interventions in the course of transmission of image, and artifacts throughout compression and processing of image frequently degrades the images by impulse noise (Plataniotis & Venetsanopoulos). Salt-and-pepper (SAP) noise is communal in natural images, with maximum and minimum intensities. In an 8-bit gray level image, this shows up as white and black points that look like “salt” and “pepper,” individually. SAP noise is created for the most part during the procedure of image capture and storage, because of false localities in memory and broken image sensors.
In image enhancement, de-noising of images is required for several image processing applications (Pitas & Venetsanopoulos, 1992; Tukey, 1977; Roy & Laskar, 2017; Oussama et al., 2018; Aboali et al., 2018). There are three forms of strategies used for reducing impulse noise in color images: channel-wise strategies, vector filtering strategies and various other strategies such as PDE based (Kim, 2006), wavelet domain based (Brook 2015), soft computing based (Schulte et al., 2006; Nair & Raju, 2012) and machine learning based (Roy & Laskar, 2016). Channel-wise strategies put on algorithms used for grayscale images to de-noise individual color channels (Koschan & Abidi, 2008). The classical vector filters are the median type filters (Chanu & Singh, 2016) with vector median filter (VMF) (Astola et al., 1990), basic vector directional filter (BVDF) (Trahanias & Venetsanopoulos, 1993), and directional distance filter (DDF) (Karakos & Trahanias, 1997). These filters work out median vectors which depend on various gathered vector distance checking principles. However, those filters result in smoothing and destroying the structures of edges & info, since it creates all pixels further irrespective of whether the pixels are noisy or not. The naivest resolution concerning this problem is the recognition of each pixel as noisy or not after which restoration of most effective the noisy pixels. This thoughtful method is well known as “Switching Vector Median Filter (SVMF)” (Smolka & Chydzinski, 2005; Celebi & Aslandogan, 2008; Celebi et al., 2010; Matsuoka et al., 2016).