DOI QR코드

DOI QR Code

영역 분할을 이용한 변형된 스위칭 필터에 관한 연구

A Study on Modified Switching Filter Using Region Segmentation

  • Kwon, Se-ik (Pukyong National University Department of Control and Istrumentation Engineering) ;
  • Kim, Nam-ho (Pukyong National University Department of Control and Istrumentation Engineering)
  • 투고 : 2016.08.01
  • 심사 : 2016.10.19
  • 발행 : 2016.10.31

초록

최근, 디지털 영상처리는 방송, 통신, 컴퓨터 그래픽, 의학 분야 등에서 많이 응용되고 있으며, 일반적으로 영상 데이터는 전송하는 과정에서 잡음이 발생한다. 영상에 첨가되는 잡음에는 다양한 종류가 있으며, salt and pepper 잡음, AWGN, 복합잡음이 대표적이다. 따라서 본 논문에서는 영상에 첨가된 복합잡음의 영향을 완화하기 위하여 훼손된 영상을 네 개의 영역으로 세분화하고 각 화소들의 잡음 종류를 추정하여 salt and pepper 잡음과 AWGN으로 나누어 처리하는 스위칭 필터를 제안하였다. 국부 마스크의 중심화소가 salt and pepper 잡음에 훼손된 경우, 세분화된 영역의 히스토그램 확률 가중치 마스크를 이용하여 처리하였으며, AWGN으로 훼손된 경우, 세분화된 영역의 분산을 이용하여 각 영역의 분산에 따라 가중치를 다르게 적용하여 가중치 필터를 제안하였다. 그리고 제안한 필터의 성능 평가를 위해 PSNR을 이용하여 기존의 방법들과 비교하였다.

Recently, digital image processing is applied a lot to the broadcasting, communication, computer graphic, and medical sectors. It generates noise when data is transmitted. There are many kinds of noises that add to the image such as salt and pepper noise, AWGN, and complex noise. Thus, this study divides the corrupted image into four4 areas and estimates the types of noises each pixel, and this study suggested a switching filter that separates the estimated into salt and pepper noise and AWGN. In the case that center pixel of local mask is corrupted by salt and pepper noise, it used a histogram probability weighting of subdivided area. Also, in case that it is corrupted by AWGN, algorithm that is applied to with different weights given for the distribution of each area with using subdivided area's distribution was suggested. For an objective comparison and conclusion, this study used PSNR and compared to existing methods.

키워드

참고문헌

  1. K. N. Plataniotis and A. N. Venetsanopoulos, Color Image Processing and Applications, 1st Ed., Berlin, Germany: Springer, 2000.
  2. R. C. Gonzalez and R. E. woods, Digital Image Processing, 3rd Ed., Upper Saddle River, NJ: Prentice Hall, 2008.
  3. Y. J. Chang and Y. S. Ho, "Stereo matching using distance transform and 1D array kernel," J. KICS, vol. 41, no. 5, pp. 546-554, May 2016. https://doi.org/10.7840/kics.2016.41.5.546
  4. Y. H. Kim, D. W. Lim, and Y.-S. Kim, "Design of fluctuation function to improve BER performance of data hiding in encrypted image," J. KICS, vol. 41, no. 3, pp. 307-316, Mar. 2016. https://doi.org/10.7840/kics.2016.41.3.307
  5. W. Kim, J. Shin, and B. T. Oh, "Region-based error concealment of depth map in multiview video," J. KICS, vol. 40, no. 12, pp. 2530-2538, Dec. 2015. https://doi.org/10.7840/kics.2015.40.12.2530
  6. S. I. Kwon and N. H. Kim, "A study on modified spatial weighted filter in mixed noise environments," JKIICE, vol. 19, no. 1, pp. 237-243, Jan. 2015.
  7. R. Oten 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
  8. J. Wang and J. Hong, "A new selt-adaptive weighted filter for removing noise in infrared images," IEEE ICIECS, pp. 1-4, Dec. 2009.
  9. T. Azetsu, N. Suetake, and E. Uchino, "Trilateral filter using rank order information of pixel value for mixed gaussian and impulsive noise removal," ISPACS, pp. 303-306, Nov. 2013.
  10. T. Bai and J. Tan, "Automatic detection and removal of high-density impulse noises," IET, Image Processing, vol. 9, no. 2, pp. 162-172, Feb. 2015. https://doi.org/10.1049/iet-ipr.2014.0286