Journal of the Institute of Electronics and Information Engineers (전자공학회논문지)

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

DOI QR Code

Spectral Reflectance Estimation based on Similar Training Set using Correlation Coefficient

상관 계수를 이용한 유사 모집단 기반의 분광 반사율 추정

  • Yo, Ji-Hoon (School of Electronics Engineering, Kyungpook National University) ;
  • Ha, Ho-Gun (School of Electronics Engineering, Kyungpook National University) ;
  • Kim, Dae-Chul (School of Electronics Engineering, Kyungpook National University) ;
  • Ha, Yeong-Ho (School of Electronics Engineering, Kyungpook National University)
  • Received : 2013.05.21
  • Published : 2013.10.25
Download PDF
⟨ Previous Next ⟩

Abstract

In general, a color of an image is represented by using red, green, and blue channels in a RGB camera system. However, only information of three channels are limited to estimate a spectral reflectance of a real scene. Because of this, the RGB camera system can not accurately represent the color. To overcome this limitation and represent an accurate color, researches to estimate the spectral reflectance by using a multi-channel camera system are being actively proceeded. Recently, a reflectance estimation method adaptively constructing a similar training set from a traditional training set according to a camera response by using a spectral similarity was introduced. However, in this method, an accuracy of the similar training set is reduced because the spectral similarity based on an average and a maximum distances was applied. In this paper, a reflectance estimation method applied a spectral similarity based on a correlation coefficient is proposed to improve the accuracy of the similar training set. Firstly, the correlation coefficient between the similar training set and the spectral reflectance obtained by Wiener estimation method is calculated. Secondly, the similar training set is constructed from the traditional training set according to the correlation coefficient. Finally, Wiener estimation method applied the similar training set is performed to estimate the spectral reflectance. To evaluate a performance of the proposed method with previous methods, experimental results are compared. As a result, the proposed method showed the best performance.

일반적으로 영상의 색은 RGB 카메라 시스템의 red, green, blue 채널들을 사용하여 재현된다. 하지만 세 채널들의 정보만으로 실제 장면의 분광 반사율을 추정하는데 한계가 있다. 이 때문에 RGB 카메라 시스템은 색을 정확하게 재현하지 못한다. 이 한계를 극복하고 정확한 색을 재현하기 위해 다채널 카메라 시스템을 사용하여 분광 반사율을 추정하는 연구들이 활발히 진행되고 있다. 최근 분광 유사도를 사용하여 카메라 응답에 따라 기존 모집단에서 유사 모집단을 적응적으로 구성하는 분광 반사율 추정법이 소개되었다. 하지만 이 방법에는 평균 거리와 최대 거리 기반의 분광 유사도가 적용되었기 때문에 유사 모집단의 정확도가 저하된다. 본 논문에서는 유사 모집단의 정확도를 향상시키기 위해 상관 계수 기반의 분광 유사도가 적용된 분광 반사율 추정법을 제안하였다. 먼저 기존 모집단과 위너(Wiener) 추정법을 통해 획득된 분광 반사율 간의 상관 계수를 계산한다. 다음으로 상관 계수에 따라 기존 모집단에서 유사 모집단을 구성한다. 마지막으로 유사 모집단이 적용된 위너 추정법을 수행하여 분광 반사율을 추정한다. 제안된 방법과 이전의 방법들의 성능을 평가하기 위해 실험 결과를 비교하였다. 그 결과, 제안한 방법이 제일 우수한 성능을 나타내었다.

Keywords

References

  1. J. Y. Hardeberg, "Acquisition and reproduction of color images: colorimetric and multispectral approaches," Ph. D. dissertation, 2001.
  2. N. Shimano, K. Terai, and M. Hironaga, "Recovery of spectral reflectances of objects being imaged by multispectral cameras," Journal of Optical Society America A, Vol. 24, no. 10, pp. 3211-3219, 2007. https://doi.org/10.1364/JOSAA.24.003211
  3. H. L. Shen, J. H. Xin, and S. J. Shao, "Improved reflectance reconstruction for multispectral imaging by combining different techniques," Optics Express, Vol. 15, no. 9, pp. 5531-5536, 2007. https://doi.org/10.1364/OE.15.005531
  4. O. S. Kwon, C. H. Lee, K. H. Park, and Y. H. Ha, "Surface reflectance estimation using the principal components of similar colors," Journal of Imaging Science and Technology, Vol. 51, no. 2, pp. 166-174, 2007. https://doi.org/10.2352/J.ImagingSci.Technol.(2007)51:2(166)
  5. F. Agahian, S. A. Amirshahi, and S. H. Amirshahi, "Reconstruction of reflectance spectra using weighted principal component analysis," Color Research and Application, Vol. 33, no. 5, pp. 360-371, 2008. https://doi.org/10.1002/col.20431
  6. X. Zhang and H. Xu, "Reconstruction spectral reflectance by dividing spectral space and extending the principal components in principal component analysis," Journal of Optical Society America A, Vol. 25, no. 2, pp. 371-378, 2008. https://doi.org/10.1364/JOSAA.25.000371
  7. J. M. DiCarlo and B. A. Wandell, "Spectral estimation theory: beyond linear but before Bayesian," Journal of Optical Society America A, Vol. 20, no. 7, pp. 1261-1270, 2003. https://doi.org/10.1364/JOSAA.20.001261
  8. J. L. Nieves, E. M. Valero, S. M. C. Nascimento, J. H. Andres, and J. Romero, "Multispectral synthesis of daylight using a commercial digital CCD camera," Applied Optics, Vol. 44. no. 27, pp. 5696-5703, 2005. https://doi.org/10.1364/AO.44.005696
  9. H. Haneishi, T. Hasegawa, A. Hosoi, Y. Yokoyama, N. Tsumura, and Y. Miyake, "System design for accurately estimating the spectral reflectance of art painting," Applied Optics, Vol. 39, no. 35, pp. 6621-6632, 2000. https://doi.org/10.1364/AO.39.006621
  10. Y. Murakami, T. Obi, M. Yamaguchi, N. Ohyama, and Y. Komiya, "Spectral reflectance estimation from multi-band image using color chart," Optics Communications, Vol. 188, no. 1-4, pp. 47-54, 2001. https://doi.org/10.1016/S0030-4018(00)01131-7
  11. N. Shimano, "Recovery of spectral reflectances of objects being imaged without prior knowledge," IEEE Transactions on Image Processing, Vol. 15, no. 7, pp. 1848-1856, 2006. https://doi.org/10.1109/TIP.2006.877069
  12. P. Stigell, K. Miyata, and M. Hauta-Kasari, "Wiener estimation method in estimating of spectral reflectance from RGB images," Pattern Recognition and Image Analysis, Vol. 17, no. 2, pp. 233-242, 2007. https://doi.org/10.1134/S1054661807020101
  13. H. L. Shen and J. H. Xin, "Spectral characterization of color scanner by adaptive estimation," Journal of Optical Society America A, Vol. 21, no. 7, pp. 1125-1130, 2004. https://doi.org/10.1364/JOSAA.21.001125
  14. H. L. Shen and J. H. Xin, "Spectral characterization of a color scanner based on optimized adaptive estimation," Journal of Optical Society America A, Vol. 23, no. 7, pp. 1566-1569, 2006. https://doi.org/10.1364/JOSAA.23.001566
  15. H. L. Shen, P. Q. Cai, S. J. Shao, and J. H. Xin, "Reflectance reconstruction for multispectral imaging by adaptive Wiener estimation," Optics Express, Vol. 15, no. 23, pp. 15545-15554, 2007. https://doi.org/10.1364/OE.15.015545
  16. J. Cohen, "Dependency of the spectral reflectance curves of the Munsell color chips," Psychonomic Science, Vol. 1, no. 12, pp. 369-370, 1964. https://doi.org/10.3758/BF03342963