대한전자공학회논문지SP (Journal of the Institute of Electronics Engineers of Korea SP)

대한전자공학회 (The Institute of Electronics and Information Engineers)
권호 표지

변형된 영상 생성 모델을 이용한 칼라 영상 보정

Color Image Rendering using A Modified Image Formation Model

  • 최호형 (경북대학교 모바일통신학과) ;
  • 윤병주 (경북대학교 전자공학부)
  • Choi, Ho-Hyoung (Department of Mobile Communication, Kyungpook National University) ;
  • Yun, Byoung-Ju (School of Electronics Engineering, IT College, Kyungpook National University)
  • 투고 : 2010.05.19
  • 심사 : 2010.08.30
  • 발행 : 2011.01.25
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

이미징 파이프라인(imaging pipeline)의 목적은 디스플레이 되는 영상을 원영상과 비슷하게 변환하는 것이다. 이를 위해 감마 조정 혹은 히스토그램기반 방법이 영상대비와 세부 영역을 개선하기 위해 제안되었다. 그러나 이러한 방법들은 조도성분과 색도성분이 위치에 따라 변화하므로 영상 개선에 한계가 있다. 따라서 MSR (Multi-Scale Retinex) 기법이 제안되었으며, 이는 영상에 따른 가우시안 필터의 크기에 의존하며, 독립적인 로그 신호를 기반으로 한다. 그러므로 영상 보정 후 후광효과(Halo), 색상변화(Color change or graying-out), 특정 색상의 두드러짐 등의 영상 왜곡(image distortion)이 발생한다. 따라서 본 논문에서는 영상을 전역조명성분, 국부조명성분, 반사성분으로 나누는 새로운 색상 보정 방법을 제안한다. 제안한 방법에서 전역조명성분은 가우시안 필터를 작용하여 획득하며, 국부 조명성분은 JND(Just-noticeable difference)기반 적응적 필터를 적용하여 획득한다. 반사성분은 원 영상에 획득된 전역조명성분과 국부조명성분으로 나누어 줌으로써 획득된다. 개선된 영상은 멱함수(power function)를 수행한 후 이들의 곱으로 획득되며, sRGB로 표현된다. 실험 결과에서 제안한 방법이 기존의 방법에 비해 우수한 성능을 보인다.

The objective of the imaging pipeline is to transform the original scene into a display image that appear similar, Generally, gamma adjustment or histogram-based method is modified to improve the contrast and detail. However, this is insufficient as the intensity and the chromaticity of illumination vary with geometric position. Thus, MSR (Multi-Scale Retinex) has been proposed. the MSR is based on a channel-independent logarithm, and it is dependent on the scale of the Gaussian filter, which varies according to input image. Therefore, after correcting the color, image quality degradations, such as halo, graying-out, and dominated color, may occur. Accordingly, this paper presents a novel color correction method using a modified image formation model in which the image is divided into three components such as global illumination, local illumination, and reflectance. The global illumination is obtained through Gaussian filtering of the original image, and the local illumination is estimated by using JND-based adaptive filter. Thereafter, the reflectance is estimated by dividing the original image by the estimated global and the local illumination to remove the influence of the illumination effects. The output image is obtained based on sRGB color representation. The experiment results show that the proposed method yields better performance of color correction over the conventional methods.

키워드

참고문헌

  1. J. M. Dicarlo and B. A. Wandell, "Rendering high dynamic range images," Proceedings of the SPIE: Image Sensors, Vol. 3965, pp. 189-198, Jan. 2000.
  2. I. S. Jang, K. H. Park, and Y. H. Ha, "Color Correction by Estimation of Dominant Chromaticity in Multi-Scaled Retinex," Journal of Image Science and Technology, Vol. 53, No. 5 , pp. 050502-05502-11, Aug. 2009. https://doi.org/10.2352/J.ImagingSci.Technol.2009.53.5.050502
  3. M. Ebner, Color Constancy, Wiley, London, 2007.
  4. D. J. Jobson, Z. Rahman, and G. Woodell, "Properties and performance of a center/surround retinex," IEEE Transactions on Image Processing, Vol. 6, No. 3, pp. 451-462, Mar. 1997. https://doi.org/10.1109/83.557356
  5. B. Funt, F. Ciurea, and J. McCann, "Retinex in MATLAB," Journal of Electronic Imaging, Vol. 13, No. 1, pp. 48-57, Jan. 2004. https://doi.org/10.1117/1.1636761
  6. L. Meylan, and S. Susstrunk, "High Dynamic Range Image Rendering With a Retinex-based Adaptive Filter," IEEE Transactions on Image processing, Vol. 15, No. 9, pp. 2820-30, Sep. 2006. https://doi.org/10.1109/TIP.2006.877312
  7. D. J. Jobson, Z. Rahman and G. A. Woodell , "A Multiscale Retinex for Bridging the Gap between Color Images and The Human Observation," IEEE Transactions on Image Processing, Vol. 6, No. 7 pp. 965-976, July 1997. https://doi.org/10.1109/83.597272
  8. K. Barnard, and B. Funt, "Investigations into Multi-scale Retinex," In colour imaging Vision and Technology, pp. 9-17, 1999.
  9. M. Elad, "Retinex by Two Bilateral Filters," Scale-space 2005, Vol. LNCS 3459, pp.217-229, Apr. 2005.
  10. 최두현, 장익훈, 김남철, "개선된 영상 생성 모델에 기반한 칼라 영상 향상," 전자공학회 논문지, 제46권 SP편, 6호, 2006년.
  11. B. W. Keelan, Handbook of Image Quality Characterization and Prediction, New York Basel, 2002.
  12. R. C. Gonzalez and R. E. Wood, Digital Image Processing, second edition Addison Wesley, 2002.
  13. P. J. Burt and E. H. Adelson, "The Laplacian Pyramid as a Compact Image Code," IEEE Transactions on communications, Vol. COM-31, pp. 532-540, Apr. 1983.
  14. D. J. Jobson, Zia-Ur Rahman, G. A. Woodell, and G.D. Hines. , "A Comparison of Visual Statistics for the Image Enhancement of FORESITE Aerial Images with Those of Major image Class," Visual Information Processing XIV, Proceeding of SPIE 6246, 2006.
  15. O. S. Kwon, Y. H. Cho, and Y. H. Ha, "Illumination Estimation Based on Valid Pixel Selection from CCD Camera Response," Journal of ImagingScience and Technology, Vol. 49, No. 3, pp. 308-316, May 2005.
  16. V. C. Cardei, B. Funt, and K. Barnard, "Estimating the scene illumination chromaticity by using a neural network," Journal of the optical society of America A, Vol. 19, No. 12, pp. 2374-2386, Dec. 2002. https://doi.org/10.1364/JOSAA.19.002374
  17. H. R. Kang, Computational Color Technology, SPIE, 2006.
  18. http://www.cis.rit.edu/mcsl/icam/hdr/rit_hdr/