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변형된 오차확산을 이용한 컬러 영상의 콘트라스트 개선

Contrast enhancement of color images using modified error diffusion

  • Lee, Ji-Won (Department of Electronic Engineering, Sogang University) ;
  • Park, Rae-Hong (Department of Electronic Engineering, Sogang University)
  • 발행 : 2008.09.30

초록

본 논문에서는 변형된 오차확산 (ED: error diffusion)을 이용한 새로운 컬러 영상의 콘트라스트 개선 (CE: contrast enhancement) 알고리즘을 제안하였다. 기존의 컬러 히스토그램 평활화 (HE: histogram equalization)를 하면 콘트라스트가 개선된 영상에 잘못된 윤곽선 (false contour)과 같은 왜곡 현상들 (artifacts)이 생긴다. 변형된 ED를 이용한 제안하는 CE 알고리즘은 HE 부분과 ED의 두 부분으로 나눌 수 있다. 첫 번째 부분에서는 기존의 HE 방법으로 낮은 콘트라스트를 갖는 입력 영상의 콘트라스트를 개선하였고 두 번째 부분에서는 제안한 변형된 ED를 사용하였다. 두 번째 부분의 입력들은 낮은 콘트라스트를 갖는 원래의 컬러 입력 영상과 HE 영상의 평균과 차 영상이다. 이 때 원래의 컬러 입력 영상과 HE 영상 간의 차는 스케일링되어 ED에 의해 주변으로 확산된다. 제안한 방법에서 변형된 ED 기법은 HE 영상에서 생긴 왜곡 현상을 줄이고 더 많은 수의 컬러 값을 사용할 수 있도록 한다. 낮은 콘트라스트를 갖는 많은 영상들에 대해 실험한 결과에서, 제안한 CE 알고리즘의 결과가 probability mass function 뿐만 아니라 시각적인 측면에서 더 좋은 화질을 가짐을 보였다. 제안한 CE 알고리즘은 낮은 콘트라스트의 컬러 입력 영상을 CE 하면서 동시에 왜곡 현상을 줄이기 위한 다양한 디스플레이 장치에서의 후처리 기법으로 사용될 수 있다.

This paper proposes a novel contrast enhancement (CE) algorithm for color images using the modified error diffusion (ED). After conventional color histogram equalization (HE), artifacts such as false contours are produced in the contrast enhanced image. The proposed CE algorithm using the modified ED consists of two parts: CE and ED. In the first part, a low-contrast input image is enhanced by the conventional HE method. In the second part, we use the modified ED algorithm. The inputs of the second part are the average and scaled difference images of the original color input image and the HE image, in which the scaled color difference image is diffused by the ED algorithm. In the proposed algorithm, the modified ED algorithm reduces the artifacts produced in the HE image, and increases the number of color levels. Computer simulations with a number of low-contrast color images show the effectiveness of the proposed CE method in terms of the visual quality as well as the probability mass function. It can be used as a post-processing for CE with simultaneous artifact reduction in various display devices.

키워드

참고문헌

  1. R. C. Gonzalez and R. E. Woods, Digital Image Processing. Second ed., Upper Saddle River, NJ, USA: Prentice-Hall, 2002
  2. K. Jain, Fundamentals of Digital Image Processing. Englewood Cliffs, NJ, USA: Prentice-Hall, 1989
  3. N. Bassiou and C. Kotropoulos, "Color image histogram equalization by absolute discounting back-off," Computer Vision and Image Understanding, vol. 107, pp. 108-122, Jan. 2007 https://doi.org/10.1016/j.cviu.2006.11.012
  4. I. Pitas and P. Kiniklis, "Multichannel techniques in color image enhancement and modeling," IEEE Trans. Image Process., vol. 5, no. 1, pp. 168-171, Jan. 1996 https://doi.org/10.1109/83.481684
  5. P. E. Trahanias and A. N. Venetsanopoulos, "Color image enhancement through 3-D histogram equalization," in Proc. 11th IAPR Conf. Pattern Recogn., vol. 3, pp. 545-548, Toronto, Canada, Sept. 1992
  6. S. K. Naik and C. A. Murthy, "Hue-preserving color image enhancement without gamut problem," IEEE Trans. Image Process., vol. 12, no. 12, pp. 1591-1598, Dec. 2003 https://doi.org/10.1109/TIP.2003.819231
  7. R. Kimmel, M. Elad, D. Shaked, R. Keshet, and I. Sobel, "A variational framework for retinex," Int. J. Comput. Vis., vol. 52, no. 1, pp. 7-23, Jan. 2003 https://doi.org/10.1023/A:1022314423998
  8. S. K. Nayar and V. Branzoi, "Adaptive dynamic range imaging: Optical control of pixel exposures over space and time," in Proc. IEEE Int. Conf. Computer Vision and Pattern Recognition, vol. 2, pp. 1168−1175, Nice, France, Oct. 2003
  9. S. Daly and X. Feng, "Decontouring: prevention and removal of false contour artifacts," in Proc. SPIE Human Vision and Electronic Imaging IX, vol. 5259, pp. 130-149, San Jose, CA, USA, June 2004
  10. J. W. Lee and R.-H. Park, "Contrast enhancement using modified error diffusion," in Proc. EUSIPCO 2008, accepted for publication, Lausanne, Switzerland, Aug. 2008
  11. R. W. Floyd and L. Steinberg, "An adaptive algorithm for spatial grayscale," in SID Int. Symp. Dig. Tech. Papers, pp. 36-37, 1975
  12. J. Jarvis and C. Roberts, "A new technique for displaying continuous tone images on a bilevel display," IEEE Trans. Commun., vol. 24, pp. 891-898, Aug. 1976 https://doi.org/10.1109/TCOM.1976.1093397
  13. P. Stucki, "MECCA-A multiple-error correction computation algorithm for bi-level image hard copy reproduction," Research Report RZ1060, IBM Res. Lab., Zurich, Switzerland, 1981
  14. X. Li, "Edge-directed error diffusion," IEEE Signal Processing Letters, vol. 13, no. 11, pp. 688-690, Nov. 2006 https://doi.org/10.1109/LSP.2006.879465
  15. T. Liu, "Probabilistic error diffusion for image enhancement," IEEE Trans. Consumer Electronics, vol. 53, no. 2, pp. 528-534, May 2007 https://doi.org/10.1109/TCE.2007.381725
  16. K. T. Knox, "Evolution of error diffusion," Jour. Electron Imaging, vol. 8, no. 4, pp. 422-429, Oct. 1999 https://doi.org/10.1117/1.482710
  17. N. Dantera-Venkata and B. L. Evans, "Design and analysis of vector color error diffusion halftoning systems," IEEE Trans. Image Process., vol. 10, pp. 1552-1565, Oct. 2001 https://doi.org/10.1109/83.951540