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 with Spatial Characteristics in Mixed Noise Environment

복합 잡음 환경에서 공간적 특성을 고려한 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 : 2018.11.02
  • Accepted : 2018.11.18
  • Published : 2019.03.31
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Abstract

Recently, the importance of signal processing has become gradually significant, as the frequency of video media increases in various fields. However, numerous kinds of noise generated in the transmission and reception processes can possibly affect the signal information, and the noise removal is for that reason essential as a preprocessing step. In this paper, we propose an algorithm to remove the mixed noise which is composed of impulse noise and AWGN. This algorithm is used for image restoration by noise judgment for efficient noise removal in a complex noise environment, and the noise is removed by considering spatial characteristics and pixel variations. Simulation results show that unlike existing methods, the algorithm has excellent noise cancellation characteristics by minimizing both noise effects and consequently eliminating the mixed noise; for objective judgment, we compared and analyzed the data using PSNR and profile.

최근 다양한 분야에서 영상매체의 사용 빈도가 증가함에 따라 신호처리의 중요성이 높아지고 있다. 하지만 송수신 과정에서 많은 종류의 잡음이 발생하며 신호의 정보에 영향을 미치고 있으며, 이러한 이유로 잡음 제거를 전처리 과정으로서 필수적으로 행한다. 본 논문에서는 임펄스 잡음과 AWGN이 혼합된 잡음을 제거하기 위한 알고리즘을 제안하였다. 제안한 알고리즘은 복합 잡음 환경에서 효율적인 잡음 제거를 위해 잡음 판단을 통해 영상 복원을 진행하며, 공간적 특성과 화소 변화를 고려하여 잡음을 제거한다. 시뮬레이션 결과 제안한 알고리즘은 기존 방법과 달리 두 잡음의 영향을 모두 최소화하여 잡음을 제거하여 우수한 잡음제거 특성을 나타내었으며, 객관적인 판단을 위해 PSNR 및 프로파일 등을 이용하여 비교 및 분석하였다.

Keywords

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Fig. 1 Section mask of proposed algorithm

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Fig. 2 Flow-chart of proposed algorithm

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Fig. 3 Enraged boat image with mixed noise with profile (a) Original image (b) Noise image ( σ = 10, P = 30%)

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Fig. 4 Enlarged filtering boat image with profile (a) A-TMF (b) WF (c) CWMF (d) PFA

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Fig. 5 PSNR with AWGN (a) Boat image (b) Peppers image

Table. 1 PSNR comparison or each filter(Boat)

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Table. 2 PSNR comparison or each filter(Peppers)

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