JACIII Vol.24 No.1 pp. 123-133
doi: 10.20965/jaciii.2020.p0123


IDT and Color Transfer-Based Color Calibration for Images Taken by Different Cameras

Yoshiaki Ueda*, Hideaki Misawa**, Takanori Koga***, Noriaki Suetake*, and Eiji Uchino*

*Graduate School of Science and Technology for Innovation, Yamaguchi University
1677-1 Yoshida, Yamaguchi, Yamaguchi 753-8512, Japan

**Department of Electrical Engineering, National Institute of Technology, Ube College
2-14-1 Tokiwadai, Ube, Yamaguchi 755-8555, Japan

***Department of Electrical Engineering, National Institute of Technology, Tokuyama College
Gakuendai, Shunan, Yamaguchi 745-8585, Japan

February 8, 2018
October 28, 2019
January 20, 2020
color calibration, camera calibration, color transfer

Images of the same object taken by multiple different cameras should have the same color reproduction. However, the images sometimes show different color reproduction due to the individual differences of cameras or internal camera parameters automatically determined when the images are taken. Conventional color transfer methods can be used for unifying the color reproduction of images by transforming the color distribution of an image to that of a reference image. However, conventional methods do not always lead to a good color reproduction and sometimes result in the loss of color impression of original images. In the present paper, we propose a color calibration method for images of the same object taken by different cameras. Two color transfer methods are combined to realize color calibration without the loss of color impression of an original image. Resultant images obtained by the color transfer methods are appropriately mixed into an output image. In experiments, the proposed method is applied to a variety of images and the effectiveness of the proposed method is confirmed by subjective and objective evaluations.

Cite this article as:
Y. Ueda, H. Misawa, T. Koga, N. Suetake, and E. Uchino, “IDT and Color Transfer-Based Color Calibration for Images Taken by Different Cameras,” J. Adv. Comput. Intell. Intell. Inform., Vol.24 No.1, pp. 123-133, 2020.
Data files:
  1. [1] Z. Gu, X. Su, Y. Liu, and Q. Zhang, “Local stereo matching with adaptive support-weight, rank transform and disparity calibration,” Pattern Recognition Letters, Vol.29, No.9, pp. 1230-1235, 2008.
  2. [2] C. Cigla and A. A. Alatan, “Information permeability for stereo matching,” Signal Processing: Image Communication, Vol.28, No.9, pp. 1072-1088, 2013.
  3. [3] C.-F. Juang, G.-C. Chen, C.-W. Liang, and D. Lee, “Stereo-camera-based object detection using fuzzy color histograms and a fuzzy classifier with depth and shape estimations,” Applied Soft Computing, Vol.46, pp. 753-766, 2016.
  4. [4] I. Brilakis, H. Fathi, and A. Rashidi, “Progressive 3D reconstruction of infrastructure with videogrammetry,” Automation in Construction, Vol.20, No.7, pp. 884-895, 2011.
  5. [5] M.-D. Yang, C.-F. Chao, K.-S. Huang, L.-Y. Lu, and Y.-P. Chen, “Image-based 3D scene reconstruction and exploration in augmented reality,” Automation in Construction, Vol.33, pp. 48-60, 2013.
  6. [6] C. Sung and P. Y. Kim, “3D terrain reconstruction of construction sites using a stereo camera,” Automation in Construction, Vol.64, pp. 65-77, 2016.
  7. [7] E. Reinhard, M. Ashikhmin, B. Gooch, and P. Shirley, “Color transfer between images,” IEEE Computer Graphics and Application, Vol.21, No.5, pp. 34-41, 2001.
  8. [8] Y. Mogi and A. Taguchi, “Color calibration method by histogram matching with color space transformation,” IEICE Technical Report, Vol.109, No.78, pp. 25-29, 2009 (in Japanese).
  9. [9] F. Pitié, A. C. Kokaram, and R. Dahyot, “Automated colour grading using colour distribution transfer,” Computer Vision and Image Understanding, Vol.107, No.1-2, pp. 123-137, 2007.
  10. [10] R. C. Gonzalez and R. E. Woods, “Digital Image Processing 2nd Edition,” Prentice Hall, 2001.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on May. 19, 2024