Browse > Article
http://dx.doi.org/10.6109/jkiice.2018.22.11.1468

Noise Removal Algorithm using Standard Deviation and Estimation in AWGN Environment  

Cheon, Bong-Won (Dept. of Control and Instrumentation Eng., Pukyong National University)
Kim, Nam-Ho (Dept. of Control and Instrumentation Eng., Pukyong National University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.22, no.11, 2018 , pp. 1468-1473 More about this Journal
Abstract
The importance of communication and data processing is increasing with the advance of the Fourth Industrial Revolution. Hence, the importance of video and data processing technologies, which directly influence the accuracy and reliability of equipment, is also increasing. In this research report we propose an algorithm for calculating the final output by estimating the standard deviation and estimate required for removing AWGN while adapting to changes in the frequency factors of video. This algorithm calculates the final output by checking an estimated value against the effective pixel range, which is obtained from the standard deviation of mask factors. Subsequently, the weighted value is computed, taking into account the filter output. To evaluate the functionality of this algorithm, it is compared with the most-commonly used present method through simulation. The simulation results show that the important features of the image are preserved and efficient noise cancellation performance is demonstrated.
Keywords
AWGN; Standard deviation; Noise removal; PSNR;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
연도 인용수 순위
1 D. J. Kim, P. L. Manjusha, "Assessment of Risks in Management Factors," Asia-pacific Journal of Convergent Research Interchange, HSST, ISSN : 2508-9080, vol. 1, no. 2, pp. 1-10, Jun. 2015.
2 L. Gopal, and Z. Zang, "Kalman filtering for SNR estimation in AWGN and fading channels," in 2009 IEEE 9th Malaysia International Conference on Communications, Kuala Lumpur : Malaysia, pp. 805-808, 2009.
3 P. Srisaiprai, W. Lee, and V. Patanavijit, "An alternative technique using median filter for image reconstruction based on partition weighted sum filter," in International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, Chiang Mai : Thailand, pp. 1-6, 2016.
4 S. Lahmiri, and M. Boukadoum, "Hybrid Wiener and Partial Differential Equations Filter for Biomedical Image Denoising," in IEEE International New Circuits and Systems Conference, Vancouver : Canada, pp. 26-29, 2016.
5 J. J. Hwang, K. H. Rhee, "Gaussian filtering detection based on features of residuals in image forensics," in 2016 IEEE RIVF International Conference on Computing & Communication Technologies, Research, Innovation, and Vision for the Future, Hanoi : Vietnam, pp. 153-157, 2016.
6 X. Long, N. H. Kim, "An Improved Weighted Filter for AWGN Removal," Journal of the Korea Institute of Information and Communication Engineering, vol. 17, no. 5, pp. 1227-1232, May. 2013.   DOI
7 S. I. Kwon, and N. H. Kim, "Image Restoration Algorithm Considering Pixel Distribution in AWGN Environments," Journal of the Korea Institute of Information and Communication Engineering, vol. 19, no. 7, pp. 1687-1693, Jul. 2015.   DOI
8 A. Eghbali, H. Johansson, and O. Gustafsson, "Optimal least-squares FIR digital filters for compensation of chromatic dispersion in digital coherent optical receivers," Journal of Lightwave Technology, vol. 32, no. 8, pp. 1449-1456, Apr. 2014.   DOI
9 T. Bhattacharya, and A. Chatterjee, "Evaluating performance of some common filtering techniques for removal of gaussian noise in images," in 2017 IEEE International Conference on Power, Control, Signals and Instrumentation Engineering, Chennai : India, pp. 1981-1984, 2017.
10 C. Y. Lee, and N. H. Kim, "A Study on Modified Mask for Edge Detection in AWGN Environment," Journal of the Korea Institute of Information and Communication Engineering, vol. 17, no. 9, pp. 2199-2205, Sep. 2013.   DOI