Journal of the Korea Institute of Information and Communication Engineering (한국정보통신학회논문지)

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
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Noise Removal using Gaussian Distribution and Standard Deviation in AWGN Environment

AWGN 환경에서 가우시안 분포와 표준편차를 이용한 잡음 제거

  • Cheon, Bong-Won (Dept. of Control and Instrumentation Eng., Pukyong National University) ;
  • Kim, Nam-Ho (Dept. of Control and Instrumentation Eng., Pukyong National University)
  • Received : 2019.03.11
  • Accepted : 2019.04.12
  • Published : 2019.06.30
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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.

잡음 제거는 영상 처리의 선행 과정에서 필수적으로 이루어지며, 잡음의 종류와 영상의 환경에 따라 다양한 기법들이 연구되고 있다. 그러나 기존 AWGN(additive white gaussian noise) 제거 기법들은 고주파 성분이 많은 영상에 대해 블러링 현상을 일으키며 다소 부족한 성능을 보인다. 따라서 본 논문에서는 영상의 AWGN 제거 과정에서 블러링 현상을 최소화하기 위한 알고리즘을 제안하였다. 제안한 알고리즘은 마스크 내부 화소 특성에 따라 고주파 성분필터와 저주파 성분 필터를 설정하며, 기준치에 입력 영상을 가감하여 각 필터의 출력을 계산한다. 최종 출력은 두 필터의 출력에 표준편차와 가우시안 분포를 통해 계산된 가중치를 적용한 것을 합산하여 구한다. 제안한 알고리즘은 기존 방법에 비해 AWGN 제거 성능이 우수하였으며, 시뮬레이션을 통해 이를 확인하였다.

Keywords

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Fig. 1 Gaussian distribution

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Fig. 2 Flow-chart of proposed algorithm

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Fig. 3 Test image

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Fig. 4 Simulation result

Table. 1 PSNR comparison for Baboon image

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Table. 2 PSNR comparison for Barbara image

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