Specularity Removal in Images and Videos: A PDE Approach

  • Satya P. Mallick
  • Todd Zickler
  • Peter N. Belhumeur
  • David J. Kriegman
Conference paper

DOI: 10.1007/11744023_43

Part of the Lecture Notes in Computer Science book series (LNCS, volume 3951)
Cite this paper as:
Mallick S.P., Zickler T., Belhumeur P.N., Kriegman D.J. (2006) Specularity Removal in Images and Videos: A PDE Approach. In: Leonardis A., Bischof H., Pinz A. (eds) Computer Vision – ECCV 2006. ECCV 2006. Lecture Notes in Computer Science, vol 3951. Springer, Berlin, Heidelberg

Abstract

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.

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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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