DOI QR코드

DOI QR Code

AWGN Removal using Edge Information of Local Mask

국부 마스크의 에지 정보를 이용한 AWGN 제거

  • 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 : 2016.08.30
  • Accepted : 2016.09.13
  • Published : 2017.01.31

Abstract

Recently, as demand of video processor unit rapidly increases, excellent quality of the video has been required. However, generally, video data occurs the quick flame of video due to various external causes in process of acquisition, treatment, and transmission, and major cause of the quick flame of the video is known as the noise. There are various kinds of noise, which are added to the video, AWGN is a typical one. Thus, this thesis suggested algorithm that treats in three methods by scale of the edge through using edge information of local masks. In case that edge pixel is big, it applied spatial weighting according to equation of straight line about direction of edge pixel. In case that edge pixel is middle, it suggested algorithm with spatial weighting filter and average filter, and for the smooth territory, it suggested algorithm that treats with average filter.

최근, 디지털 영상처리 장치에 대한 수요가 급격히 증대되면서 영상의 우수한 화질이 요구되고 있다. 그러나 일반적으로 영상 데이터는 획득, 처리, 전송 과정에서 여러 외부 원인에 의해 영상의 열화가 발생되며, 영상 열화의 주된 원인은 잡음에 의한 것으로 알려져 있다. 영상에 첨가되는 잡음에는 다양한 종류가 있으며 AWGN이 대표적이다. 따라서 본 논문에서는 국부 마스크의 에지 정보를 이용하여 에지의 크기에 따라 세 가지 방법으로 처리하는 알고리즘을 제안하였다. 에지 화소들의 크기가 큰 경우, 에지 화소들의 방향에 대해 직선에 방정식에 따라 공간 가중치를 적용하여 처리하였으며 중간인 경우, 공간 가중치 필터 및 평균 필터, 평활한 영역에서는 평균 필터로 처리하는 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, "A Study on the Spatial Weighted Filter in AWGN Environment," JICCE, vol. 17, no. 3, pp.724-729, Mar. 2013.
  4. X. Long and N. H. Kim, "An Improved Weighted Filter for AWGN Removal," JICCE, vol. 17, no. 5, pp. 1227-1232, May 2013.
  5. X. Long and N. H. Kim, "A Study on Image Restoration Filter in AWGN Environments," JICCE, vol. 18, no. 4, pp.949-956, Apr. 2014.
  6. Y. Gao and N. H. Kim, "A Study on Improved Denoising Algorithm for Edge Preservation in AWGN Environments," JICCE, vol. 16, no. 8, pp.1773-1778, Aug. 2012.
  7. J. Wang and J. Hong, "A New Selt-Adaptive Weighted Filter for Removing Noise in Infrared images," in Proceeding of IEEE Information Engineering and Computer Science, Wuhan, China, pp.1-4, Dec. 2009.
  8. N. Zhou and S. Zhang, "An Adaptive Image Mixed Noise Removal Algorithm Based on MMTD," in Proceeding of Digital Information Processing, Data Mining, and Wireless Communications, Moscow, Russia, pp.93-98, July 2016.
  9. K. Vasanth, K. Kumar, S. Saravanan, "Decision Based Unsymmetrical Trimmed Mode Filter for th Removal of Salt and Pepper Noise in Images," in Proceeding of 2015 International Conference on Control, Instrumentation, Communication and Computational Technologies, Abu Dhabi, pp.1-7, May 2015.