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Bayer Pattern Demosaicking Using Local-Correlation Approach

  • Rastislav Lukac
  • Konstantinos N. Plataniotis
  • Anastasios N. Venetsanopoulos
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3039)

Abstract

A new Bayer pattern demosaicking scheme for single-sensor digital cameras is introduced. The raw output from a sensor, mostly a charge coupled device (CCD) or a complementary metal oxide semiconductor (CMOS) sensor, with a Bayer filter represents a mosaic of red, green and blue pixels of different intensity. To interpolate the two missing color components in each spatial location and constitute the full color, camera output, the proposed method utilizes edge-sensing interpolation and correction steps. Since the correction step is suitable only for the image regions with high spectral correlation, otherwise is counter productive, the scheme is adaptively controlled through the comparisons between the correlation coefficient and the pre-determined parameter. The proposed method yields excellent performance, in terms of subjective and objective image quality measures, and outperforms previously developed CFA interpolation solutions.

Keywords

Mean Square Error Complementary Metal Oxide Semiconductor Correction Step Alternative Projection Color Filter Array 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Adams, J.: Design of practical color filter array interpolation algorithms for digital cameras. In: Proc. of the SPIE, vol. 3028, pp. 117–125 (1997)Google Scholar
  2. 2.
    Bayer, B.E.: Color imaging array. U.S. Patent 3 971 065 (1976)Google Scholar
  3. 3.
    Cai, C., Yu, T.H., Mitra, S.K.: Saturation-based adaptive inverse gradient interpolation for Bayer pattern images. IEE Proceedings - Vision, Image, Signal Processing 148, 202–208 (2001)CrossRefGoogle Scholar
  4. 4.
    Cok, D.R.: Signal processing method and apparatus for producing interpolated chrominance values in a sampled color image signal. U.S. Patent 4 642 678 (1987)Google Scholar
  5. 5.
    Freeman, W.T.: Median filter for reconstructing missing color samples. U.S. Patent 5 373 322 (1988)Google Scholar
  6. 6.
    Gunturk, B., Altunbasak, Y., Mersereau, R.: Color plane interpolation using alternating projections. IEEE Trans. Image Processing 11, 997–1013 (2002)CrossRefGoogle Scholar
  7. 7.
    Hur, B.S., Kang, M.G.: High definition color interpolation scheme for progressive scan CCD image sensor. IEEE Trans. Consumer Electronics 47, 179–186 (2001)CrossRefGoogle Scholar
  8. 8.
    Kehtarnavaz, N., Oh, H.J., Yoo, Y.: Color filter array interpolation using color correlation and directional derivatives. Journal of Electronic Imaging 12, 621–632 (2003)CrossRefGoogle Scholar
  9. 9.
    Kimmel, R.: Demosaicing: image reconstruction from color CCD samples. IEEE Trans. Image Processing 8, 1221–1228 (1999)CrossRefGoogle Scholar
  10. 10.
    Longere, P., Zhang, X., Delahunt, P.B., Brainard, D.H.: Perceptual assessment of demosaicing algorithm performance. Proceedings of the IEEE 90, 123–132 (2002)CrossRefGoogle Scholar
  11. 11.
    Pei, S.C., Tam, I.K.: Effective color interpolation in CCD color filter arrays using signal correlation. IEEE Trans. Circuits and Systems for Video Technology 13, 503–513 (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Rastislav Lukac
    • 1
  • Konstantinos N. Plataniotis
    • 1
  • Anastasios N. Venetsanopoulos
    • 1
  1. 1.The Edward S. Rogers Sr. Dept. of Electrical and Computer EngineeringUniversity of TorontoTorontoCanada

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