Scientific Research and Essays Vol. 6(26), pp. 5523-5533, 9 November, 2011 DOI: 10.5897/SRE11.856 Available online at http://www.academicjournals.org/SRE ISSN 1992-2248 © 2011 Academic Journals Full Length Research Paper An efficient implementation of switching median filter with boundary discriminative noise detection for image corrupted by impulse noise Haidi Ibrahim*, Theam Foo Ng and Sin Hong Teoh School of Electrical and Electronic Engineering, Engineering Campus, Universiti Sains Malaysia, 14300 Nibong Tebal, Penang, Malaysia. Accepted 18 October, 2011 Switching Median Filter with Boundary Discriminative Noise Detection (BDND) is one of the useful methods that are capable to restore digital images which have been extremely corrupted by universal impulse noise. Following the fundamental framework of the switching median filter, the construction of BDND can be divided into two stages. The first stage classifies the pixels into either “noise” or “noise- free” pixels, while the second stage restores the image by changing only the intensity values of the “noise” pixels. Unfortunately, the originally proposed BDND employs sorting operations in both of its stages. This condition makes the originally proposed BDND computationally expensive. Therefore, in this paper, an implementation of BDND with reduced computational time is suggested. This reduction is achieved mainly by manipulating the local histograms’ properties. Experimental results show that the proposed implementation successfully produces the same results as the originally proposed BDND, but with much shorter processing time. Key words: Digital image processing, impulse noise, median filter. INTRODUCTION One of the noises that are commonly corrupting digital images is the impulse noise. This type of noise can be contributed by many factors such as by faulty imaging sensors and from malfunction memory cells in storage devices. Besides, impulse noise also can be created during the transmission of the signal through noisy channel (Chan et al., 2005). In general, the impulse noise can be considered as an additive noise. This noise changes the value of some pixels at random locations into either relatively high or relatively low intensity value. Noise pixels with high intensity values appear as white dots on the image (that is salt), while noise pixels with *Corresponding author. E-mail: haidi_ibrahim@ieee.org. low intensity values appear as black dots on the image (that is pepper). Therefore, impulse noise is also named as the salt-and-pepper noise (Petrou and Bosdogianni, 2000). As the impulse pixels are having a relatively high contrast toward their surrounding, even at low percentage of corruption, the impulse noise can degrade the appearance of the image significantly (Ibrahim et al., 2008). Therefore, it is crucial for us to remove the impulse noise before any subsequent image processing operations such as image segmentation and pattern recognition. Commonly, median filter which is a nonlinear filter is employed to reduce the impulse noise in digital images due to its sensitivity towards outliers (Eng and Ma, 2001). The standard median filter (SM) operates by defining a contextual region by using a sliding window of size W W. It replaces the intensity value of the centre