Journal of Theoretical and Applied Information Technology 10 th January 2014. Vol. 59 No.1 © 2005 - 2014 JATIT & LLS. All rights reserved . ISSN: 1992-8645 www.jatit.org E-ISSN: 1817-3195 103 AN ADAPTIVE THRESHOLD INTENSITY RANGE FILTER FOR REMOVAL OF RANDOM VALUE IMPULSE NOISE IN DIGITAL IMAGES 1 S.SARAVANAKUMAR, 2 A.EBENEZER JEYAKUMAR, 3 K.N.VIJEYAKUMAR, 4 NELSON KINGSLEY JOEL 1,3 Department of ECE, ANNA UNIVERSITY REGIONAL CENTRE, COIMBATORE 2 Director Academics, SRI RAMAKRISHNA ENGINEERING COLLEGE, COIMBATORE 4 PG Scholar, Department of ECE, ANNA UNIVERSITY REGIONAL CENTRE, COIMBATORE E-mail: 1 sskaucbe@gmail.com, 2 ebeyjkumar@rediffmail.com, 3 vijey.tn@gmail.com, 4 joelnov20@gmail.com ABSTRACT A novel approach for denoising digital images corrupted by impulse noise is presented in this brief. The proposed approach uses an efficient technique to identify pixels corrupted by random noise. This is done by setting an intensity range for the center pixel of the selected window and checking whether the number of pixels which fall within this range is above or below a specified threshold. If the condition for an uncorrupted pixel fails in the selected window, the window size is increased and threshold is adaptively changed. Experimental evaluation using MATLAB revealed that the proposed approach demonstrates better Peak Signal to Noise Ratio (PSNR) improvement for higher noise densities when compared to the best of the approaches used for comparison. Visual interpretation of output images revealed that our approach preserved edges and fine details when compared to the existing algorithms. Keywords: Random Valued Impulse Noise, Intensity Range, Soft-switching, Rank order, Peak Signal to Noise Ratio. 1. INTRODUCTION AND RELATED WORK Digital images are highly corrupted by Impulse noise while during acquisition and transmission. The impulse noise can be classified under: salt and pepper noise and random valued noise. The pixel which is identified as corrupted and takes either maximum or minimum gray level is classified as pixel corrupted by salt and pepper noise. The corrupted pixel which takes any value between 0 and 255 is classified as Random Valued Impulse Noise (RVIN). Further processing of an image for its enhancement needs this noise to be removed; otherwise the performances of image processing tasks such as segmentation, feature extraction, object recognition, etc. are severely degraded by noise [1]. Though there are various algorithms for removal of RVIN they are not efficient at high noise densities. So we concentrate on design of efficient algorithm for RVIN removal in images. Eng H.L and Ma [2] proposed a Noise Adaptive Soft-switching Median (NASM) filter. The filter uses a soft-switching noise-detection scheme to identify each pixel’s characteristic, followed by proper filtering operation. In the noise- detection scheme, global (i.e., based on the entire picture) and local (i.e., based on a small window) pixel statistics are utilized in the first and the remaining two decision-making levels respectively. Chen and Hong Ren Wu [3] proposed an Adaptive Center Weighted Median Filter (ACWMF). Jianjun Zhang[4] proposed a two phase median filter for removal of RVIN. The filter removes impulse noise from degraded images in 2 phases. In the first phase adaptive Center Weighted Median Filter (CWMF)[12] is used to identify noisy pixels. In the noise removal phase he used an iterative method based on median value. Crnojevic et al proposed a median filter which performs filtering operation on a pixel to pixel basis. The proposed approach considers median of absolute deviations to identify the corrupted pixel. The basic principle of the proposed