Super-resolution Sharpening-Demosaicking with Spatially Adaptive Total-Variation Image Regularization

  • Takahiro Saito
  • Takashi Komatsu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3767)


We previously presented a demosaicking method that simultaneously removes image blurs caused by an optical low-pass filter used in a digital color camera with the Bayer’s RGB color filter array. Our prototypal sharpening-demosaicking method restored only spatial frequency components lower than the Nyquist frequency corresponding to the mosaicking pattern, but it often produced ringing artifacts near color edges. To overcome this difficulty, this paper introduces the super-resolution into the prototypal method. First, we for mulate the recovery problem in the DFT domain, and then introduce the super-resolution by the total-variation (TV) image regularization into the sharpening-demosaicking approach. The TV-based super-resolution effectively demosaics sharp color images while preserving such image structures as intensity values are almost constant along edges, without producing ringing artifacts, but it tends to flatten signal variations excessively in texture image regions. To remedy the drawback, furthermore we introduce a spatially adaptive technique that controls the TV image regularization according to the saliency of color edges around a pixel.


Color Image Color Edge Nyquist Frequency Ringing Artifact Recovery Problem 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Hamilton Jr., J.F., Adams, J.E.: Adaptive color plan interpolation in signal sensor color electronic camera. United State Patent 5, 629–734 (1997)Google Scholar
  2. 2.
    Kimmel, R.: Demosaicking: image reconstruction from color CCD samples. IEEE Trans. Image Processing 7(3), 1221–1228 (1999)CrossRefMathSciNetGoogle Scholar
  3. 3.
    Chang, E., Cheung, S., Pan, D.Y.: Color filter array recovery using a threshold-based variable number of gradients. In: Proc. SPIE, vol. 3650, pp. 36–43 (1999)Google Scholar
  4. 4.
    Gunturk, B.K., Altunbasak, Y., Mersereau, R.M.: Color plan interpolation using alternating projections. IEEE Trans. Image Processing 11(9), 997–1013 (2002)CrossRefGoogle Scholar
  5. 5.
    Lu, W., Tan, Y.-P.: Color filter array demosaicking: new method and performance measure. IEEE Trans. Image Processing 12(10), 1194–1210 (2003)CrossRefGoogle Scholar
  6. 6.
    Wu, X., Zhang, N.: Primary-consistent soft-decision color demosaicking for digital cameras. IEEE Trans. Image Processing 13(9), 1263–1274 (2004)CrossRefGoogle Scholar
  7. 7.
    Komatsu, T., Saito, T.: Demosaicking for a color image sensor with removal of blur due to an optical low-pass filter. In: Proc. SPIE, vol. 5301, pp. 334–345 (2004)Google Scholar
  8. 8.
    Malgouyres, F., Guichard, G.: Edge direction preserving image zooming: a mathematical and numerical analysis. J. Num. Anal. 39(1), 1–37 (2001)zbMATHCrossRefMathSciNetGoogle Scholar
  9. 9.
    Rudin, L., Osher, S., Fetami, E.: Nonlinear total variation based noise removal algorithm. Physica D 60, 259–268 (1992)zbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Takahiro Saito
    • 1
  • Takashi Komatsu
    • 1
  1. 1.Department of Electrical, Electronics and Information Engineering, High-Tech Research CenterKanagawa UniversityYokohamaJapan

Personalised recommendations