Advertisement

Color Kernel Regression for Robust Direct Upsampling from Raw Data of General Color Filter Array

  • Masayuki Tanaka
  • Masatoshi Okutomi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6494)

Abstract

Upsampling with preserving image details is highly demanded image operation. There are various upsampling algorithms. Many upsampling algorithms focus on the gray image. For color images, those algorithms are usually applied to a luminance component only, or independently applied channel by channel. However, we can not observe the full-color image by a single image sensor equipped in a common digital camera. The data observed by the single image sensor is called raw data. The raw data is converted into the full-color image by demosaicing. Upsampling from the raw data requires sequential processes of demosaicing and upsampling. In this paper, we propose direct upsampling from the raw data based on a kernel regression. Although the kernel regression is known as powerful denoising and interpolation algorithm, the kernel regression has been also proposed for the gray image. We extend to the color kernel regression which can generate the full-color image from any kind of raw data. Second key point of the proposed color kernel regression is a local density parameter optimization, or kernel size optimization, based on the stability of the linear system associated to the kernel regression. We also propose a novel iteration framework for the upsampling. The experimental results demonstrate that the proposed color kernel regression outperforms existing sequential approaches, reconstruction approaches, and existing kernel regression.

Keywords

Sequential Approach Kernel Regression Luminance Component Color Artifact PSNR Comparison 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Takeda, H., Farsiu, S., Milanfar, P.: Kernel Regression for Image Processing and Reconstruction. IEEE Transactions on Image Processing 16, 349–366 (2007)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Fattal, R.: Image upsampling via imposed edge statistics. ACM Transactions on Graphics (TOG) (26)Google Scholar
  3. 3.
    Shan, Q., Li, Z., Jia, J., Tang, C.: Fast image/video upsampling. In: ACM SIGGRAPH Asia 2008 papers, pp. 1–7. ACM, New York (2008)Google Scholar
  4. 4.
    Freeman, W., Jones, T., Pasztor, E.: Example-based super-resolution. IEEE Computer Graphics and Applications, 56–65 (2002)Google Scholar
  5. 5.
    Glasner, D., Bagon, S., Irani, M.: Super-Resolution from a Single Image. In: IEEE International Conference on Conputer Vision (ICCV) (2009)Google Scholar
  6. 6.
    Zhang, X., Wu, X.: Image Interpolation by Adaptive 2D Autoregressive Modeling and Soft-Decision Estimation. IEEE Transactions on Image Processing 17, 887–896 (2008)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Kopf, J., Cohen, M., Lischinski, D., Uyttendaele, M.: Joint bilateral upsampling. ACM Transactions on Graphics (26)Google Scholar
  8. 8.
    Bayer, B.: Color imaging array (1976)Google Scholar
  9. 9.
    Li, X., Gunturk, B., Zhang, L.: Image demosaicing: A systematic survey (Visual Communications and Image Processing) (2008)Google Scholar
  10. 10.
    Gunturk, B., Glotzbach, J., Altunbasak, Y., Schafer, R., Mersereau, R.: Demosaicking: color filter array interpolation. IEEE Signal Processing Magazine 22, 44–54 (2005)CrossRefGoogle Scholar
  11. 11.
    Wu, X., Zhang, N.: Primary-consistent soft-decision color demosaicking for digital cameras. IEEE Transactions on image processing 13, 1263–1274 (2004)CrossRefGoogle Scholar
  12. 12.
    Hirakawa, K., Parks, T.: Adaptive homogeneity-directed demosaicing algorithm. IEEE Transactions on Image Processing 14, 360–369 (2005)Google Scholar
  13. 13.
    Gotoh, T., Okutomi, M.: Direct super-resolution and registration using raw CFA images. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), vol. 2, pp. 600–607 (2004)Google Scholar
  14. 14.
    Farsiu, S., Elad, M., Milanfar, P.: Multiframe demosaicing and super-resolution of color images. IEEE Transactions on Image Processing 15, 141–159 (2006)CrossRefGoogle Scholar
  15. 15.
    Levin, A., Fergus, R., Durand, F., Freeman, W.: Image and depth from a conventional camera with a coded aperture. ACM Transactions on Graphics 26, 70 (2007)Google Scholar
  16. 16.
    Nadaraya, E.: On estimating regression. Theory of Probability and its Applications 9, 141 (1964)CrossRefzbMATHGoogle Scholar
  17. 17.
    Silverman, B.: Density estimation for statistics and data analysis (1986)Google Scholar
  18. 18.
    Chatterjee, P., Milanfar, P.: A generalization of non-local means via kernel regression. In: Proc. of SPIE Conf. on Computational Imaging (2008)Google Scholar
  19. 19.
    Dabov, K., Foi, A., Egiazarian, K.: Image restoration by sparse 3D transform-domain collaborative filtering. In: Proc. SPIE Electronic Imaging (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Masayuki Tanaka
    • 1
  • Masatoshi Okutomi
    • 1
  1. 1.Tokyo Institute of TechnologyJapan

Personalised recommendations