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Removal of Random Valued Impulse Noise by Directional Mean Filter using Statistical Noise based Detection
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 46 - Number 10 |
| Year of Publication: 2012 |
| Authors: R. Seetharaman, R. Vijayaragavan |
10.5120/6943-9220
|
R. Seetharaman, R. Vijayaragavan . Removal of Random Valued Impulse Noise by Directional Mean Filter using Statistical Noise based Detection. International Journal of Computer Applications. 46, 10 ( May 2012), 14-18. DOI=10.5120/6943-9220
Abstract
Digital image processing and video processingare mainly used for the noise removal technique is a highly demanded research field. The main objective of this paper work is to detect and de-noise the image using a novel two stage algorithm for the removal of random valued impulse noise from the images is presented. In this paper, the first stage does the noise pixels are detected using the average deviations and threshold value. In the second stage,the directional differences between the four main directions are calculated. The median values of the pixels which lie in the direction of minimum difference are calculated and the noisy pixel values are replaced with the median value. This method identifies the noisy pixels in the corrupted images. So the two stage algorithm for image denoising random valued impulse noise is used. Experimental results show that proposed method is superior to the conventional methods in Peak Signal Noise Ratio (PSNR) values of the image performs, with and without noise compared. Comparison of PSNR values of the proposed method and the existing techniques show that the proposed method perform better than the existing one.
References
- X. Zhang and Y. Xiong. 2009. Impulse noise removal using directional difference based noise detector and adaptive weighted mean filter. IEEE Signal Process. Lett. , Vol. 16, no. 4, (Apr. 2009) pp. 295–298.
- Y. Dong and S. Xu. 2007. A new directional weighted median filter for removal of random-value impulse noise. IEEE Signal Process. Lett. , vol. 14, pp. 193–196.
- R C Gonzalez and R E Woods. 2007. Digital Image Processing. Prentice-Hall, India, second edition edition.
- W. Luo. 2007. An efficient detail-preserving approach for removing impulse noise in images. IEEE Signal Process. Lett. , vol. 13, pp. 413–417.
- G. Pok, J. Liu, and A. S. Nair. 2003. Selective removal of impulse noise based on homogeneity level information. IEEE Trans. Image Processing, Vol. 12, pp. 85 -92.
- S. J. Ko and Y. H. Lee. 1991. Center weighted median filters and their applications to image enhancement. IEEE Trans. Circuits Syst. , vol. 38,pp. 984–993.
- D. Brownrigg. 1984. The weighted median filter. Commun. Assoc. Comput. Mach. , vol. 27, pp. 807–818.
- Crnojevi. C. V, Senk. Vand Trpovski. Z 2004. Advanced impulse detection based on pixel-wise MAD. IEEE Signal Processing Letters, Vol. 11,No. 6, pp. 589–592. .
- Chui. C, Garnett. R, Huegerich. T, and He. W. 2005. A universal noise removal algorithm with an impulse detector. IEEE Trans on Image Processing, Vol. 14,No. 5, pp. 1747–1754.
- Ko . S. J. and Lee S. J. , 1991. Adaptive centre weighted median filters and their applications to image enhancement. IEEE Transactions on Circuits and Systems, Vol. 15, No. 6, pp. 984–993.
- Bovik A. C. 2000. Nonli near filtering for image analysis and enhancement. Handbook of Image & Video Processing. San Diego,CA: Academic, ch. 3. 2 (G. R. Arce), pp. 95–95.
- Alliney S and Morandi C. 1986. Digital image registration using projections. IEEE Trans. Pattern Anal. Mach. Intell. , Vol. 8, No. 2, pp. 222– 233.
- Y. Dong, R. H. Chan, and S. Xu. 2007. A detection statistic for random-valued impulse noise. IEEE Trans. Image Process. , vol. 16, pp. 1112–1120.
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