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A Study on Image Restoration Filter in AWGN Environments

AWGN 환경에서 영상복원 필터에 관한 연구

  • Xu, Long (Dept. of Control and Instrumentation Eng., Pukyong National University) ;
  • Kim, Nam-Ho (Dept. of Control and Instrumentation Eng., Pukyong National University)
  • Received : 2014.01.23
  • Accepted : 2014.02.26
  • Published : 2014.04.30

Abstract

Recently, with the development of hardware and software technology related with image information delivery, the demand for various multimedia service has increased. But, the process of treating, sending, and storing image signals generates image degradation by various external causes. The main cause of image degradation is noise. image is mostly damaged by AWGN (additive white Gaussian noise). Therefore, there have been active researches on noise elimination. This paper, to reduce the effects of AWGN added to the image, suggests a noise-eliminating algorithm which is excellent in low-frequency and high-frequency characteristics in space. And, this paper, through simulation techniques, compared the result of the suggested algorithm with those of the existing methods. And, to evaluate the performance of it, PSNR (peak signal to noise ratio) was used.

최근, 영상정보에 관련된 하드웨어 및 소프트웨어 등의 기술이 발달함에 따라 다양한 멀티미디어 서비스에 대한 수요가 증가 되고 있다. 그러나 영상 신호의 처리, 전송, 저장하는 과정에서 여러 외부 원인에 의해 영상의 열화가 발생되며, 영상 열화의 주된 원인은 잡음에 의한 것이다. 영상은 대부분 AWGN(additive white Gaussian noise)에 의해 훼손되며, 잡음 제거를 위한 연구가 활발히 진행되고 있다. 본 논문에서는 영상에 첨가되는 AWGN의 영향을 완화하기 위하여, 공간 영역에서의 저주파 및 고주파 특성이 우수한 잡음제거 알고리즘을 제안하였다. 그리고 시뮬레이션을 통해 기존의 방법들과 결과를 비교하였으며, 성능의 평가를 위하여 PSNR(peak signal to noise ratio)을 사용하였다.

Keywords

References

  1. K. N. Plataniotis and A. N. Venetsanopoulos, Eds., Colir Image Processing and Applications, Springer, Berlin, Germany, 2000.
  2. R. C. Gonzalez and R.E. woods, Eds., Digiral Image Processing, Prentice Hall, 2007.
  3. Jiahui Wang and Jingxing Hong, "A New Self-Adaptive Weighted Filter for Removing Noise in Infrared images", IEEE Information Engineering and Computer Science, ICIECS International Conference, 2009.
  4. Gao Yinyu and Nam-Ho Kim, "A Study on Improved Denoising Algorithm for Edge Preservation in AWGN Environments", Journal of KIICE, vol. 16, no. 8, pp. 1773-1778, Aug. 2012. https://doi.org/10.6109/jkiice.2012.16.8.1773
  5. Y. Dong and S. Xu, "A New Directional Weighted Median Filter for Removal Random-Valued Impulse Noise", IEEE Signal Processing Lett., vol 14, no. 3, pp. 193-196, 2007. https://doi.org/10.1109/LSP.2006.884014
  6. Oten, Remzi and De Figueiredo, Rlui J P, "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
  7. Wei Wang and Peizhong LU, "Adaptive switching anisotropic diffusion model for universal noise removal", Intelligent Control and Automation (WCICA), 2012 10th World Congress on , pp. 4803-4808, 2012.
  8. He Changwei, Liu Yingxia, Ren Wenjie and Wang Xin, "Wavelet denoising based on multistage median filtering", Journal of Computer Application, vol. 27, no. 9, pp. 2117- 2119, Sep. 2007.
  9. Gao Yinyu and Nam-Ho Kim, "A study on image restoration for removing mixed noise while considering edge information", International Journal of KIICE, vol. 15, no. 10, pp. 2239- 2246, Oct. 2011. https://doi.org/10.6109/jkiice.2011.15.10.2239

Cited by

  1. 복합잡음 환경에서 영상복원 필터에 관한 연구 vol.18, pp.8, 2014, https://doi.org/10.6109/jkiice.2014.18.8.2001
  2. AWGN 환경에서 고주파 성분을 고려한 잡음 제거 알고리즘 vol.22, pp.6, 2018, https://doi.org/10.6109/jkiice.2018.22.6.867