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

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
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A Study on Multiple Filter for Mixed Noise Removal

복합잡음 제거를 위한 다중 필터에 관한 연구

  • Kwon, Se-Ik (Dept. of Control and Instrumentation Eng., Pukyong National University) ;
  • Kim, Nam-Ho (Dept. of Control and Instrumentation Eng., Pukyong National University)
  • Received : 2017.07.27
  • Accepted : 2017.09.20
  • Published : 2017.11.30
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Abstract

Currently, the demand for multimedia services is increasing with the rapid development of the digital age. Image data is corrupted by various noises and typical noise is mainly AWGN, salt and pepper noise and the complex noise that these two noises are mixed. Therefore, in this paper, the noise is processed by classifying AWGN and salt and pepper noise through noise judgment. In the case of AWGN, the outputs of spatial weighted filter and pixel change weighted filter are composed and processed, and the composite weights are applied differently according to the standard deviation of the local mask. In the case of salt and pepper noise, cubic spline interpolation and local histogram weighted filters are composed and processed. This study suggested the multiple image restoration filter algorithm which is processed by applying different composite weights according to the salt and pepper noise density of the local mask.

현재, 디지털 시대의 급속 발전과 함께 멀티미디어 서비스에 대한 수요가 증가되고 있다. 영상 데이터는 다양한 잡음에 의해 훼손되며, 주로 AWGN, salt and pepper 잡음, 이 두 잡음이 혼합된 복합잡음 등이 대표적이다. 따라서, 본 논문에서는 잡음 판단을 통해 AWGN 및 salt and pepper 잡음으로 분류하여 처리한다. AWGN인 경우, 공간 가중치 필터 및 화소 변화 가중치 필터의 출력을 합성하여 처리하며, 국부 마스크의 표준편차에 따라 합성 가중치를 다르게 적용한다. salt and pepper 잡음인 경우, 3차원 스플라인 보간법 및 국부 히스토그램 가중치 필터를 합성하여 처리하며, 국부 마스크의 salt and pepper 잡음 밀도에 따라 합성 가중치를 다르게 적용하여 처리하는 다중 영상복원 필터 알고리즘을 제안하였다.

Keywords

References

  1. R. C. Gonzalez and R. E. woods, Digital Image Processing, 3rd ed. Upper Saddle River, NJ: Prentice Hall, 2008.
  2. K. N. Plataniotis and A. N. Venetsanopoulos, Color Image Processing and Applications, 1st ed. Berlin, Germany: Springer, 2000.
  3. X. Long and N. H. Kim, "Modified Median Filter for Impulse Noise Removal," Journal of the Korea Institute of Information and Communication Engineering, vol. 17, no. 2, pp. 461-466, Feb. 2013. https://doi.org/10.6109/jkiice.2013.17.2.461
  4. S. I. Kwon and N. H. Kim, "A Study on Modified Spatial Weighted Filter in Mixed Noise Environments," Journal of the Korea Institute of Information and Communication Engineering, vol. 19, no. 1, pp. 237-243, Jan. 2015. https://doi.org/10.6109/jkiice.2015.19.1.237
  5. R. Öten and R. J. P. de Figueiredo, "Adaptive Alpha-Trimmed Mean Filters Under Deviations From Assumed Noise Model," IEEE Trans, Image Processing, vol. 13, no. 5, pp. 627-639, May 2004. https://doi.org/10.1109/TIP.2003.821115
  6. J. Wang and J. Hong, "A New Selt-Adaptive Weighted Filter for Removing Noise in Infrared Images," Information Engineering and Computer Science, Wuhan, China, pp.1-4, 2009.
  7. D. H. Kim, "SVD-based Image Enhancement Method using Weighted Average of Histogram Stretching and Eqalization," Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology, vol. 5, no. 5, pp. 77-85, Oct. 2015.
  8. S. I. Kwon and N. H. Kim, "Salt and Pepper Noise Removal using Cubic Spline Interpolation," Journal of the Korea Institute of Information and Communication Engineering, vol. 20, no. 10, pp. 1955-1960, Oct. 2016. https://doi.org/10.6109/jkiice.2016.20.10.1955
  9. S. I. Kwon and N. H. Kim, "Nonlinear Composite Filter for Gaussian and Impulse Noise Removal," Journal of the Korea Institute of Information and Communication Engineering, vol. 21, no. 3, pp. 629-635, Mar. 2017. https://doi.org/10.6109/jkiice.2017.21.3.629

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  1. S&P 잡음 환경에서 표준편차를 이용한 변형된 가중치 필터 vol.24, pp.4, 2017, https://doi.org/10.6109/jkiice.2020.24.4.474