A Study on Image Restoration Filter in Impulse Noise Environments

임펄스 잡음 환경에서 영상복원 필터에 관한 연구

  • Xu, Long (Department of Control and Instrumentation Engineering, Pukyong National University) ;
  • Kim, Nam-Ho (Department of Control and Instrumentation Engineering, Pukyong National University)
  • Received : 2013.10.23
  • Accepted : 2013.11.29
  • Published : 2014.02.28


As the society develops to advanced digital information times, many studies are underway about digital video processing technology areas such as image restoration. There are typical methods to restore the image which have been damaged by the impulse noise like SM(standard median) filter and CWM(center weighted median) filter. These filters show excellent noise reduction capabilities in low noise density areas, but in high noise density areas, noise reduction capabilities are not sufficient. In this paper, in order to restore the degraded images in impulse(Salt & Pepper) noise environment, the image restoration filter algorithm was suggested which expands and subdivide the mask focusing on damaged pixels. And to demonstrate the superiority of the proposed algorithm used PSNR (peak signal to noise ratio) as the standard of judgement.

사회가 고도의 디지털 정보화 시대로 발전함에 따라 영상복원 등 디지털 영상처리 기술분야에 관한 많은 연구가 진행되고 있다. 임펄스 잡음에 훼손된 영상을 복원하는 대표적인 방법은 SM(standard median)필터, CWM(center weighed median)필터 등이 있지만, 이들은 잡음밀도가 낮은 영역에서는 우수한 잡음 제거 특성을 나타내고, 잡음밀도가 높은 영역에서는 잡음제거 특성이 미흡하다. 본 논문에서는 임펄스(Salt & Pepper) 잡음 환경에서 훼손된 영상을 복원하기 위해 훼손된 화소를 중심으로 하여 마스크를 확장 세분화하여 처리하는 영상복원 필터 알고리즘을 제안하였다. 그리고 제안한 알고리즘의 우수성을 입증하기 위해 PSNR(peak signal to noise ratio)을 판단의 기준으로 사용하였다.


Supported by : 부경대학교


  1. R. C. Gonzalez and R.E. woods, Eds., Digiral Image Processing, Prentice Hall, 2007.
  2. K. N. Plataniotis and A. N. Venetsanopoulos, Eds., Colir Image Processing and Applications, Springer, Berlin, Germany, 2000.
  3. A. S. A wad and H. Man, "High Performance Detection Filter for Impulse Noise Removal in Images," IEEE Electronic Letters, vol. 44, no. 3, pp. 192-194, Jan. 2008.
  4. Gao Yinyu and Nam-Ho Kim, "A study cascade filter algorithm for random valued impulse noise elimination", International Journal of KIICE, vol. 15, no. 10, pp. 1177-1182, May 2011.
  5. 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.
  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.
  7. T. Chen and H. R. Wu, "Adaptive impulse detection using center weighted median filters". IEEE Signal Processing Letters, vol 8, no. 1, pp. 1-3, Jan 2001.
  8. Zhou, Y.Y. ; Ye, Z.F. ; Huang, J.J, "Improved decisionbased detail-preserving variational method for removal of random-valued impulse noise," Published in IET Image Processing, vol. 6, no. 7, pp. 978-985, May 2012.
  9. Z. Wang and D. Zhang, "Prgressice switching median filter for the removal of impulse noise from highly corrupted images," IEEE Transactions on Circuis and Systems :Analog and Digital Signal Processing, vol 46, no.1, pp. 78-80, Jan 1999.
  10. 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.
  11. 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.

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