DOI QR코드

DOI QR Code

A Study on Removal of Salt and Pepper Noise using Deformable Masks Depending on the Noise Density

잡음 밀도에 따라 가변 마스크를 적용한 Salt and Pepper 잡음 제거에 관한 연구

  • Hong, Sang-Woo (Department of Control and Instrumentation Engineering, Pukyong National University) ;
  • Kim, Nam-Ho (Department of Control and Instrumentation Engineering, Pukyong National University)
  • Received : 2015.06.19
  • Accepted : 2015.07.23
  • Published : 2015.08.20

Abstract

In digital era image processing has been utilized in a variety of media such as TV, camera and smart phone. Typically salt and pepper noise are generated by various causes during the analysis, identification, and processing of image data. Principal filters such as SMF, CWMF, and AMF have been used to remove these noise. But the existing filters fall short of edge preservation and noise elimination in high noise densities. Thus, a processing algorithm, on which the size of deformable mask varies depending on the noise density, is proposed to remove salt and pepper noise effectively in this study. The performance of the proposed method was evaluated compared with the existing methods using PSNR.

디지털 시대를 맞이하여 영상 처리는 TV, 카메라, 스마트폰 등과 같은 다양한 매체에서 활용되고 있다. 일반적으로 영상 데이터를 분석, 인식, 처리하는 과정에서 여러가지 원인에 의해 salt and pepper 잡음이 발생한다. 이러한 잡음을 제거하기 위한 대표적인 필터는 SMF, CWMF, AMF 등이 있다. 기존의 필터들은 잡음 밀도가 높은 영역에서 에지 보존 및 잡음 제거 특성이 미흡하다. 따라서 본 논문은 salt and pepper 잡음을 효과적으로 제거하기 위하여, 잡음밀도에 따라 마스크 크기를 가변하여 처리하는 알고리즘을 제안하였다. 그리고 제안한 방법의 성능을 평가하기 위해 PSNR을 사용하여 기존의 방법들과 비교하였다.

Keywords

References

  1. C. S. Cho and H. S. Kang, Multimedia signal processing, second edition, pp. 2-6, 2011.
  2. L. Xu and N. H. Kim, "Modified median filter for impulse noise removal" JKIICE, vol. 17, no. 2, pp. 461-446, 2013.
  3. R. Öten and F. De, “daptive alpha-trimmed mean filter 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
  4. T. Chan and H. Wu, “daptive impulse detection using center-weighed median filters", IEEE Letters Signal Processing, vol. 8, no. 1, pp. 1-3, Jan. 2011. https://doi.org/10.1109/97.889633
  5. B. Wei, “mproved adaptive median filtering” Journal of Computer Application, vol. 13, no. 2, pp. 1732-1734, July 2008. https://doi.org/10.3724/SP.J.1087.2008.01732
  6. S. J. Ko and Y. H. Lee, "Center weighted median filters and their application to image enhancement" IEEE Trans. Circuits system, vol. 38, no. 9, pp. 984-993, 1991. https://doi.org/10.1109/31.83870
  7. Xu Long and Nam-Ho Kim, "A study on directionally weighted filter for algorithm in impulse noise environments." JKIICE,vol. 18, no. 7, pp. 1734-1739, 2014.
  8. L. Xu and N. H. Kim, "An improved adaptive median filter for impulse noise removal " JKIICE, vol. 17, no. 4, pp. 989-995, 2013.
  9. T. Veerakumar, S. Esakkirajan and L. Vennila, “n efficient approach to remove random valued impulse noise in images" ICRTIT, 2012 International Conference on. pp. 490-503, 2012.

Cited by

  1. 표준편차 및 3차 스플라인 보간법을 이용한 영상 복원 알고리즘에 관한 연구 vol.21, pp.9, 2015, https://doi.org/10.6109/jkiice.2017.21.9.1689
  2. S&P 잡음 환경에서 표준편차를 이용한 변형된 가중치 필터 vol.24, pp.4, 2015, https://doi.org/10.6109/jkiice.2020.24.4.474