Specularity Removal in Images and Videos: A PDE Approach

  • Satya P. Mallick
  • Todd Zickler
  • Peter N. Belhumeur
  • David J. Kriegman
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3951)


We present a unified framework for separating specular and diffuse reflection components in images and videos of textured scenes. This can be used for specularity removal and for independently processing, filtering, and recombining the two components. Beginning with a partial separation provided by an illumination-dependent color space, the challenge is to complete the separation using spatio-temporal information. This is accomplished by evolving a partial differential equation (PDE) that iteratively erodes the specular component at each pixel. A family of PDEs appropriate for differing image sources (still images vs. videos), differing prior information (e.g., highly vs. lightly textured scenes), or differing prior computations (e.g., optical flow) is introduced. In contrast to many other methods, explicit segmentation and/or manual intervention are not required. We present results on high-quality images and video acquired in the laboratory in addition to images taken from the Internet. Results on the latter demonstrate robustness to low dynamic range, JPEG artifacts, and lack of knowledge of illuminant color. Empirical comparison to physical removal of specularities using polarization is provided. Finally, an application termed dichromatic editing is presented in which the diffuse and the specular components are processed independently to produce a variety of visual effects.


Color Space Diffuse Component Texture Scene Specular Component Diffuse Color 
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.


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  1. 1.
    Bajcsy, R., Lee, S., Leonardis, A.: Detection of diffuse and specular interface reflections and inter-reflections by color image segmentation. IJCV 17(3), 241–272 (1996)CrossRefGoogle Scholar
  2. 2.
    Blake, A., Brelstaff, G.: Geometry from specularities. In: ICCV, pp. 394–403 (1988)Google Scholar
  3. 3.
    Brockett, R., Maragos, P.: Evolution equations for continuous scale morphology. IEEE trans. on Sig. Proc. 42, 3377–3386 (1994)CrossRefGoogle Scholar
  4. 4.
    Crandall, M.G., Ishii, H., Lions, P.L.: User’s Guide to Viscosity Solutions of Second Order Partial Differential Equations, July 1992, vol. 26 (1992)Google Scholar
  5. 5.
    Healey, G., Binford, T.O.: Local shape from specularity. CVGIP 42(1), 62–86 (1988)Google Scholar
  6. 6.
    Klinker, G., Shafer, S., Kanade, T.: The measurement of highlights in color images. IJCV 2(1), 7–32 (1988)CrossRefGoogle Scholar
  7. 7.
    Lee, H.S.: Method for computing the scene-illuminant chromaticity from specular highlights. JOSAA 3(10), 1694–1699 (1986)CrossRefGoogle Scholar
  8. 8.
    Mallick, S., Zickler, T., Kriegman, D., Belhumeur, P.: Beyond Lambert: Reconstructing specular surfaces using color. In: CVPR, pp. II:619–626 (2005)Google Scholar
  9. 9.
    Maragos, P., Butt, M.A.: Curve evolution and differential morphology. Fundamenta Informaticae 41, 91–129 (2000)MathSciNetMATHGoogle Scholar
  10. 10.
    Nayar, S., Fang, X., Boult, T.: Separation of reflection components using color and polarization. Int. Journal of Computer Vision 21(3), 163–186 (1997)CrossRefGoogle Scholar
  11. 11.
    Osadchy, M., Jacobs, D., Ramamoorthi, R.: Using specularities for recognition. In: ICCV, pp. 1512–1519 (2003)Google Scholar
  12. 12.
    Park, J.B.: Efficient color representation for image segmentation under nonwhite illumination. In: SPIE, vol. 5267, pp. 163–174 (2003)Google Scholar
  13. 13.
    Perona, P., Malik, J.: Scale-space and edge detection using anisotropic diffusion. PAMI 12(7), 629–639 (1990)CrossRefGoogle Scholar
  14. 14.
    Ping, T., Lin, S., Quan, L., Shum, H.-Y.: Highlight removal by illumination-constrained inpainting. In: ICCV, Nice, France, pp. 164–169 (2003)Google Scholar
  15. 15.
    Shafer, S.: Using color to separate reflection components. COLOR research and applications 10(4), 210–218 (1985)CrossRefGoogle Scholar
  16. 16.
    Tan, R., Ikeuchi, K.: Reflection components decomposition of textured surfaces using linear basis functions. In: CVPR, pp. I:125–131 (2005)Google Scholar
  17. 17.
    Tan, R., Ikeuchi, K.: Separating reflection components of textured surfaces using a single image. PAMI 27(2), 178–193 (2005)CrossRefGoogle Scholar
  18. 18.
    Tan, R.T., Nishino, K., Ikeuchi, K.: Separating reflection components based on chromaticity and noise analysis. PAMI 26(10), 1373–1381 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Satya P. Mallick
    • 1
  • Todd Zickler
    • 2
  • Peter N. Belhumeur
    • 3
  • David J. Kriegman
    • 1
  1. 1.Computer Science and EngineeringUniversity of California at San DiegoUSA
  2. 2.Engineering and Applied SciencesHarvard UniversityCambridgeUSA
  3. 3.Computer ScienceColumbia UniversityNew YorkUSA

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