Reduction of Impulse Noise in Monochrome Images using Soft Computing Techniques N

Reduction of Impulse Noise in Monochrome Images using Soft Computing Techniques N

Reduction of Impulse Noise in Monochrome Images using Soft Computing Techniques N.Hemalatha (Department of Electronics and Communication Engineering) K.Kavitha ((Department of Electronics and Communication Engineering) Abstract A novel two-step fuzzy filter method that adopts a fuzzy logic approach for the improvement of images corrupted with impulse noise is presented in this paper. The method used for filtering is Fuzzy novel impulsive noise reduction method (FNINR) consists of a fuzzy noise detection mechanism and a fuzzy filtering method to remove both salt and pepper noise and random valued impulse noise from noise images. Based on the assumption of peak-signal-to-noise-ratio (PSNR) and subjective evaluations we have found experimentally, that the proposed method provides a significant improvement on other state of applications. Index Terms Fuzzy filter, Salt Pepper noise, Random valued impulse noise, Image restoration, Stable noise. INTRODUCTION Impulse noise models The Fuzzy Impulsive Noise Reduction method (FRINR) consists of two separated phases the detection phase and the filtering phase. The phase of detection is a combination of two fuzzy algorithms, which are complementary to each other and which are combined together to receive a more robust detection method. Both algorithms use fuzzy rules 27 to determine whether a pixel is corrupted with impulse noise or not. After the application of the two fuzzy algorithms, our fuzzy filtering technique focuses only on those pixels that are detected by both algorithms, i.e. the filtering is concentrated on the real impulse noise pixels. The filtering method consists of a fuzzy averaging where the weights are constructed using a predefined fuzzy set. NOISE DETECTION METHOD The proposed detection method is composed of two subunits that are both used to define corrupted impulse noise pixels. The first subunit investigates the neighborhood around a pixel to conclude if the pixel can be considered as impulsive noise or not. The second subunit uses fuzzy gradient values 28 to determine the degree in which a pixel can be considered as impulse noise and the degree in which a pixel can be considered as noise free. Detection mechanism unit also found that the g(i,j)-value could be relatively large in case of an edge pixel. Therefore we have considered the following two values denoted as a(i, j ) and b(i, j) (4) b(i,j)g(i,j) (5) If both values (a (i, j) and b (i, j)) are large, then the pixel can be considered as an edge pixel instead of a noisy one. So when the two values (a (i, j) and b (i, j)) are very similar we conclude that the pixel is noise free. Otherwise, if the difference between a (i, j) and b (i, j) is large then we consider the pixel as noisy. This can be implemented by the following fuzzy rule Rule 1. Defining when a central pixel A (i, j) is corrupted with impulse noise (6) So, c(i, j) corresponds to the g(i m, j l) coming from the most homogeneous region around A(i, j), which should correspond to the region with the smallest amount of impulse noise pixels. Experimental results have shown that the best Fig. 1. The membership function LARGE denoted as large . Fig. 2. A neighborhood of a central pixel A(i, j ). (12) (16) where f is the original image, QUOTE the restored image of size MN and size the maximum possible intensity value (with 8-bit the maximum will be 255). (a) Original Image (b) Noisy Image (c) CWM (d) FIRE (e) AWFM (f) Proposed Fig. 4. Results of Different Methods in restoring 20 percent corrupted image House. TABLE I PSNR (dB) results for House image on different level of noise densities (a)Original Image (b)Noisy Image (c) CWM (d) FIRE (e) AWFM (f) Proposed Fig. 5. Results of Different Methods in restoring 20 percent corrupted image Real. EMBED Excel.Chart.8 s Fig. 6. Comparison graph of various denoising method CONCLUSION Noise Density ()CWMFIREAWFMProposed534.0436.79 37.9438.541032.9334.89 35.8036.061531.4132.37 33.0534.722028.5930.04 32.8733.385023.0921.89 29.7530.427014.6017.03 20.1124.609011.5112.55 14.1717.82A novel fuzzy impulsive noise reduction method (FINR), which consists of two fuzzy detection methods and a fuzzy filtering algorithm, has been presented in this paper. This filter is especially developed for reducing all kind of impulsive noise (i.e., salt pepper noise and random valued impulsive noise). Its main advantage is that it removes impulsive noise very well while preserving the fine image details. Additionally, the proposed filter yields better performance when compared to other method. Experimental results have shown the feasibility of the novel filter. A numerical results, such as the PSNR(Peak signal to noise ratio, and observations (Fig. 4 and 5) have shown conculsive results for monochrome images. REFERENCES 1 K. Arakawa, Fuzzy rule-based image processing with optimization, in E.E. Kerre, M. Nachtegael (Eds.), Fuzzy Techniques in Image Processing, Springer, New York, 2000, pp. 222247. 2 A. Ben Hamza, H. Krim, Image denoising a nonlinear robust statistical approach, IEEE Trans. Signal Process. 49 (2001) 30453053. 13 E.E. Kuruoglu, C. Molina, S.J. Godsill, W.J. Fitzgerald, A new analytic representation for the -stable probability density function, in The Fifth World Meeting of the International Society for Bayesian Analysis (ISBA), Istanbul, August 1997. 19 M. Nachtegael, E.E. Kerre, Connections between binary, gray-scale and fuzzy mathematical morphologies, Fuzzy Sets and Systems 124 (2001) 7385. 20 B. Reusch, M. Fathi, L. Hildebrand, Fuzzy color processing for quality improvement, in International Forum on Multimedia and Image Processing, 1998, pp. 841848. NV3jIDRheE(yFCRjKxk_9LV aIZai-6ssueBd(UW/oZ Grb0JS v-H8ds98ZMjswdER -W nOExT7xTxj NKH/-6nB 6sw1YO [email protected]@oohtw2mJ1(C8mzTtp_. UB_H, HsgIO OO X4 VA3A JCM-PEPEP2 6156-eY8,Cjt vF4TLP EZwZM2
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