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Received Dec 29, 2017; Accepted Apr 8, 2018
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1. Introduction
Images are easily corrupted by impulse noise during the signal acquisition, transmission, or storage [1]. There are two types of impulse noise, salt and pepper noise and random valued noise. Salt and pepper noise randomly alters a certain amount of pixels into two extremes, either 0 or 255, for an 8-bit image. The noise significantly damages the image information which leads to difficulties in succeeding image processing tasks such as edge detection or image segmentation and image recognition tasks. Because the noise pixel differs from most of its local neighbours, it has large gradient value the same as the edge pixel [2]. How to effectively restore the noisy image is still a challenging problem.
Numerous techniques have been proposed to suppress the salt and pepper noise. The most simple and well-known methods are the standard median filter [3] and its variants [4–6] which are nonlinear filters whose responses are based on reordering the intensity values in the neighbourhood of the corrupted pixel. The nonlinear operation exhibits good denoising power [7], but when the noise level increases, the edges and other details of the image cannot be restored. This is because the median filter simply replaces every pixel’s value. Switching or decision based filters are proposed and have become a hotspot of removal of salt and pepper noise; their strategy in common is using a two-stage technique which first detects the possible noise pixels in an image and then replaces the noise pixels [8–19]. For example, Chan et al. used the adaptive median filter to detect the noise pixels and an objective function with an