________________________________________________________________________ ISSN (Print): 2278-5140, Volume-2, Issue – 3, 2013 27 Local median information based adaptive fuzzy filter for impulse noise removal 1 Prajnaparamita Behera, 2 Shreetam Behera 1 Final Year Student, M.Tech VLSI Design, Dept. of ECE, 2 Asst .Professor, Dept. ECE CIT, Centurion University of Technology & Management Jatni (Odisha), India Email: [email protected]Abstract— Impulse noise removal is still a great challenging job in the field of image processing. Lots of linear and nonlinear filters have been proposed earlier for the impulse noise removal but it is found that they degrade the quality of images by blurring. In this paper a two pass median filter is used to remove impulse noise. In the first pass min-max based median filter is used for detection and correction of noisy pixel. In the second pass local median information based adaptive fuzzy filter is used to denoise the image. The proposed method is efficient, fast and results in a higher PSNR (Peak Signal to Noise Ratio) values when compared to other traditional filters. Keywords: Impulse noise, blurring, Min-max based median filter, Adaptive, PSNR I. INTRODUCTION Image denoising is the most important and challenging job in the field of image processing. During the time of data acquiring, broadcasting and loading the image becomes partial. The noise is come into the images when captured by camera or scanner or while recording and when the image is transmitted by a noisy channel. Salt and pepper noise is one type of noise which is impulsive in nature and most of the techniques used for its removal has nonlinear characteristics. Median filter is the most popular nonlinear filter in image processing .The median filter is not appropriate for non-impulsive noise reduction. The Weighted Median (WM) filter is the modification of standard median filter where a specific weight is given to every pixel present in the window. CWM is a special type of weighted median filter where weight is specified only the centre pixel of the window. The standard median filter is the most popular nonlinear filter for noise reduction. But in case of large window and high noise it gives rise to more blurring as comparison to CWM. To avoid this obscuring of images a MDB filter was introduced in [1]. This proposed technique was found to be more superior than the centre weighted median filter. In [2] the authors introduced an algorithm in which the noisy pixel is replaced by trimmed median value for denoising the images and it is found to be better in comparison with the standard median filter. To produce more effective and reduced noise levels , median filter is imbibed with fuzzy technique by the authors in [3] .A switching based fuzzy scheme is introduced by the authors in [4] which is able to eliminate impulse noise from grayscale images to a greater extent. It was also seen that with the increase in the processing window more accurate result was obtained in [5].In [6], the authors proposed a novel approach to detect and remove impulse noise with an additional aim of enhancing the image. The efficiency of adaptive fuzzy filter is well demonstrated in [7] with respect to other traditional median filters. In this paper a two pass median filtering scheme is proposed for removal of impulse noise from heavily corrupted images. The proposed technique is explained in the section II. Section III analyses and explains the results of the proposed fuzzy scheme followed by the conclusion and references.. II. PROPOSED SCHEME FOR IMAGE DENOISING In this paper a two pass median filtering scheme is proposed, where in the first pass, the noise is detected and corrected using a Min-Max Based detection based median filter and then an adaptive fuzzy filter based on local window information is used in the second pass. The flow charts give a brief outline of the proposed method. In the first pass the noise detected when the pixel value is greater than the maximum of the window pixels or less than the minimum of the window pixels and it is replaced
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Local median information based adaptive fuzzy filter for ... · Fig.4-(a) Noisy cameraman image,(b)Output of CWM filter,(c)Output of median Filter,(d)Output of MDB filter,(e)Output
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International Journal of Advanced Computer Engineering and Communication Technology (IJACECT)
Fuzzy 35.8172 28.9759 24.6821 22.0393 19.6146 17.5793 15.6987 13.7142 Table 2:Comparison Table for PSNR values of Lena at various technique with different noise densities .
International Journal of Advanced Computer Engineering and Communication Technology (IJACECT)