Adaptive Color Filter Array Demosaicking Based on Constant Hue and Local Properties of Luminance

  • Chun-Hsien Chou
  • Kuo-Cheng Liu
  • Wei-Yu Lee
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4872)


Most commercial digital cameras use a single electronic sensor overlaid with a color filter array (CFA) to capture imagery. Since only one primary color is sampled in each pixel, the missing color primaries must be reconstructed by interpolation. In this paper, an adaptive demosaicking scheme for CFA interpolation is proposed. The scheme uses intra-channel correlation, color difference correlation, constant hue, and luminance-color difference correlation is proposed. A rough interpolation is first implemented by bilinear interpolation. Then the color difference correlation and constant hue are successively used to update the missing color primaries. To obtain high quality color images, an adaptive algorithm using luminance-color difference correlation and the information of edge direction is iteratively applied to improve the image quality around the edges. Simulation results demonstrate that the image quality of the proposed algorithm is better than that of the approach using color difference correlation in terms of peak signal-to-noise ratio (PSNR).


Color filter array demosaicking hue luminance 


  1. 1.
    Bayer, B.: Color Imaging Array. U.S. Patent, no. 3,971,065 (1976)Google Scholar
  2. 2.
    Pei, S.C., Tam, I.K.: Effective color interpolation in CCD color filter array using signal correlation. IEEE Trans. on Circuits Systems Video Technology 13, 503–513 (2003)CrossRefGoogle Scholar
  3. 3.
    Li, X.: Demosaicing by successive approximation. IEEE Trans. on Image Processing 14, 370–379 (2005)CrossRefGoogle Scholar
  4. 4.
    Chang, L., Tan, Y.P.: Effective use of spatial and spectral correlations for color filter array demosaicking. IEEE Trans. on Consumer Electronics 50, 355–365 (2004)CrossRefGoogle Scholar
  5. 5.
    Kimmel, R.: Demosaicing: Image reconstruction from CCD samples. IEEE Trans. on Image Processing 8, 1221–1228 (1999)CrossRefGoogle Scholar
  6. 6.
    Cok, D.R.: Signal processing method and apparatus for producing interpolated chrominance values in a sampled color image signal. U.S. Patent, no. 4,642,678 (1987)Google Scholar
  7. 7.
    Gunturk, B.K., Altunbasak, Y., Mersereau, R.M.: Color plane interpolation using alternating projections. IEEE Trans. on Image Processing 11, 997–1013 (2002)CrossRefGoogle Scholar
  8. 8.
    Li, X., Orchard, M.T.: New edge-directed interpolation. IEEE Trans. on Image Processing 10, 1521–1527 (2001)CrossRefGoogle Scholar
  9. 9.
    Chang, H.A., Chen, H.: Directionally weighted color interpolation for digital cameras. ISCAS 6, 6284–6287 (2005)Google Scholar
  10. 10.
    Alleysson, S., Susstrunk, S., Herault, J.: Linear demosaicking inspired by human visual system. IEEE Trans. on Image Processing 14, 439–449 (2005)CrossRefGoogle Scholar
  11. 11.
    Lian, N., Chang, L., Tan, Y.P.: Improved color filter array demosaicking by accurate luminance estimation. ICIP 1, 41–44 (2005)Google Scholar
  12. 12.
    Wu, X., Zhang, N.: Primary-consistent soft-decision color demosaicking for digital cameras. IEEE Trans. on Image Processing 13, 1263–1274 (2004)CrossRefGoogle Scholar
  13. 13.
    Lu, W., Tan, Y.P.: Color Filter Array Demosaicking: New Method and Performance Measures. IEEE Trans. on Image Processing 12, 1194–1210 (2003)CrossRefGoogle Scholar
  14. 14.
    Kodak Lossless True Color Image Suite (2007),

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Chun-Hsien Chou
    • 1
  • Kuo-Cheng Liu
    • 1
    • 2
  • Wei-Yu Lee
    • 1
  1. 1.Department of Electrical Engineering, Tatung UniversityTaiwan
  2. 2.Foreign Language and Information Educating Center, Taiwan Hospitality and Tourism 

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