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

  • 제22권11호
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  • Pages.1468-1473
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  • 2018
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  • 2234-4772(pISSN)
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  • 2288-4165(eISSN)
한국정보통신학회 (The Korea Institute of Information and Commucation Engineering)
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AWGN 환경에서 표준편차 및 추정치를 통한 잡음 제거 알고리즘

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)
  • 투고 : 2018.08.17
  • 심사 : 2018.09.10
  • 발행 : 2018.11.30
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초록

4차 산업혁명의 발전에 따라 통신 및 데이터 처리의 중요성이 높아지고 있으며, 이에 따라 장비의 정확성과 신뢰성에 직접적인 영향을 미치는 영상 및 데이터 처리의 중요성 또한 증가하고 있다. 본 논문에서는 영상의 주파수 성분의 변화에 적응하며 AWGN을 제거하기 위해 표준편차와 추정치의 유추를 통해 최종 출력을 산출하는 알고리즘을 제안하였다. 제안한 알고리즘은 마스크 성분의 표준편차를 통해 유효 화소 범위를 설정하여 추정치를 구하며, 가중치를 적용한 후 필터의 출력에 가감하여 최종 출력을 계산한다. 그리고 제안하는 알고리즘의 성능 평가를 위해 시뮬레이션을 통해 기존 방법과 비교 분석하였으며, 시뮬레이션 결과 영상의 중요 특성을 보존하며 효율적인 잡음 제거 성능을 보였다.

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.

키워드

HOJBC0_2018_v22n11_1468_f0001.png 이미지

Fig. 1 Example of weight mask

HOJBC0_2018_v22n11_1468_f0002.png 이미지

Fig. 2 Block diagram of proposed algorithm

HOJBC0_2018_v22n11_1468_f0003.png 이미지

Fig. 3 Barbara image with AWGN (a) Original image (b) Noise image(σ=10)

HOJBC0_2018_v22n11_1468_f0004.png 이미지

Fig. 4 Enlarged boat image with AWGN (a) GF (b) A-TMF (c) AWMF (d) PFA

HOJBC0_2018_v22n11_1468_f0005.png 이미지

Fig. 5 Enlarged difference boat image with AWGN (a) GF (b) A-TMF (c) AWMF (d) PFA

Table. 1 PSNR comparison or each filter(Barbara)

HOJBC0_2018_v22n11_1468_t0001.png 이미지

Table. 2 PSNR comparison or each filter(Boat)

HOJBC0_2018_v22n11_1468_t0002.png 이미지

참고문헌

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