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http://dx.doi.org/10.6109/jkiice.2019.23.6.675

Noise Removal using Gaussian Distribution and Standard Deviation 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.23, no.6, 2019 , pp. 675-681 More about this Journal
Abstract
Noise removal is a pre-requisite procedure in image processing, and various methods have been studied depending on the type of noise and the environment of the image. However, for image processing with high-frequency components, conventional additive white Gaussian noise (AWGN) removal techniques are rather lacking in performance because of the blurring phenomenon induced thereby. In this paper, we propose an algorithm to minimize the blurring in AWGN removal processes. The proposed algorithm sets the high-frequency and the low-frequency component filters, respectively, depending on the pixel properties in the mask, consequently calculating the output of each filter with the addition or subtraction of the input image to the reference. The final output image is obtained by adding the weighted data calculated using the standard deviations and the Gaussian distribution with the output of the two filters. The proposed algorithm shows improved AWGN removal performance compared to the existing method, which was verified by simulation.
Keywords
Noise removal; AWGN; Gaussian distribution; Standard deviation;
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Times Cited By KSCI : 3  (Citation Analysis)
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