Abstract

In this paper, we propose an efficient method to separate the diffuse and specular reflection components from a single image. The method is built on the observation that, for diffuse pixels, the intensity ratios between the maximum values and range values (maximums minus minimums) are independent of surface geometry. The specular fractions of the image pixels can then be computed by using the intensity ratio. For textured surfaces, image pixels are classified into clusters by constructing a pseudo-chromaticity space, and the intensity ratio of each cluster is robustly estimated. Unlike existing techniques, the proposed method works in a pixel-wise manner, without specular pixel identification and any local interaction. Experimental results show that the proposed method runs 4× faster than the state of the art and produces improved accuracy in specular highlight removal.

© 2013 Optical Society of America

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  1. T. Gevers and H. Stokman, “Classifying color edges in video into shadow-geometry, highlight, or material transitions,” IEEE Trans. Multimedia 5, 237–243 (2003).
  2. R. T. Tan, K. Nishino, and K. Ikeuchi, “Color constancy through inverse-intensity chromaticity space,” J. Opt. Soc. Am. A 21, 321–334 (2004).
    [CrossRef]
  3. J. Toro and B. Funt, “A multilinear constraint on dichromatic planes for illumination estimation,” IEEE Trans. Image Process. 16, 92–97 (2007).
    [CrossRef]
  4. Q. Yang, S. Wang, N. Ahuja, and R. Yang, “A uniform framework for estimating illumination chromaticity, correspondence and specular reflection,” IEEE Trans. Image Process. 20, 53–63 (2011).
    [CrossRef]
  5. S. P. Mallick, T. E. Zickler, D. J. Kriegman, and P. N. Belhumeur, “Beyond Lambert: reconstructing specular surfaces using color,” in IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005. CVPR 2005 (IEEE Computer Society, 2005), Vol. 2, pp. 619–626.
  6. D. Miyazaki, K. Hara, and K. Ikeuchi, “Median photometric stereo as applied to the Segonko tumulus,” Int. J. Comput. Vis. 86, 229–242 (2010).
    [CrossRef]
  7. H. C. Lee, D. J. Breneman, and C. O. Schulte, “Modeling light reflection for computer color vision,” IEEE Trans. Pattern Anal. Mach. Intell. 12, 402–409 (1990).
    [CrossRef]
  8. A. Artusi, F. Banterle, and D. Chetverikov, “A survey of specularity removal methods,” Comput. Graph. Forum 30, 2208–2230 (2011).
    [CrossRef]
  9. G. J. Klinker, S. A. Shafer, and T. Kanade, “The measurement of highlights in color images,” Int. J. Comput. Vis. 2, 7–32 (1988).
    [CrossRef]
  10. Y. Sato and K. Ikeuchi, “Temporal-color space analysis of reflection,” J. Opt. Soc. Am. A 11, 2990–3002 (1994).
    [CrossRef]
  11. R. T. Tan, K. Nishino, and K. Ikeuchi, “Separating reflection components based on chromaticity and noise analysis,” IEEE Trans. Pattern Anal. Mach. Intell. 26, 1373–1379 (2004).
    [CrossRef]
  12. R. T. Tan and K. Ikeuchi, “Separating reflection components of textured surfaces using a single image,” IEEE Trans. Pattern Anal. Mach. Intell. 27, 178–193 (2005).
    [CrossRef]
  13. R. T. Tan and K. Ikeuchi, “Reflection components decomposition of texured surfaces using linear basis functions,” in IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005. CVPR 2005 (IEEE Computer Society, 2005), Vol. 1, pp. 125–131.
  14. S. K. Nayar, X. S. Fang, and T. Boult, “Separation of reflection components using color and polarization,” Int. J. Comput. Vis. 21, 163–186 (1997).
    [CrossRef]
  15. S. Lin, Y. Li, S. B. Kang, X. Tong, and H.-Y. Shum, “Diffuse-specular separation and depth recovery from image sequences,” in Computer Vision—ECCV 2002 (Springer, 2002), pp. 210–224.
  16. P. Tan, S. Lin, L. Quan, and H. Y. Shum, “Highlight removal by illumination-constrained inpainting,” in Ninth IEEE International Conference on Computer Vision (IEEE, 2003), Vol. 1, pp. 164–169.
  17. S. P. Mallick, T. E. Zickler, P. N. Belhumeur, and D. J. Kriegman, “Specularity removal in images and videos: a PDE approach,” in Computer Vision—ECCV 2006 (Springer, 2006), Vol. 1, pp. 550–563.
  18. H. L. Shen, H. G. Zhang, S. J. Shao, and J. H. Xin, “Chromaticity-based separation of reflection components in a single image,” Pattern Recogn. 41, 2461–2469 (2008).
    [CrossRef]
  19. Q. Yang, S. Wang, and N. Ahuja, “Real-time specular highlight removal using bilateral filtering,” in Computer Vision–ECCV 2010 (Springer, 2010), pp. 87–100.
  20. H. L. Shen and Q. Y. Cai, “Simple and efficient method for specularity removal in an image,” Appl. Opt. 48, 2711–2719 (2009).
    [CrossRef]
  21. S. A. Shafer, “Using color to separate reflection components,” Color Res. Appl. 10, 210–218 (1985).
    [CrossRef]
  22. S. Barsky and M. Petrou, “The 4-source photometric stereo technique for three-dimensional surfaces in the presence of highlights and shadows,” IEEE Trans. Pattern Anal. Mach. Intell. 25, 1239–1252 (2003).
    [CrossRef]
  23. J. van de Weijer and T. Gevers, “Edge-based color constancy,” IEEE Trans. Image Process. 16, 2207–2214 (2007).
    [CrossRef]
  24. K. J. Yoon and I. S. Kweon, “Voting-based separation of diffuse and specular pixels,” Electron. Lett. 40, 1260–1261 (2004).
    [CrossRef]
  25. D. Xu, C. Doutre, and P. Nasiopoulos, “Correction of clipped pixels in color images,” IEEE Trans. Vis. Comput. Graph. 17, 333–344 (2011).
  26. R. T. Tan, “Specular highlight removal from a single image,” http://people.cs.uu.nl/robby/code.html .
  27. Q. Yang, “Real-time specular highlight removal using bilateral filtering,” http://www.cs.cityu.edu.hk/~qiyang/ .

2011

Q. Yang, S. Wang, N. Ahuja, and R. Yang, “A uniform framework for estimating illumination chromaticity, correspondence and specular reflection,” IEEE Trans. Image Process. 20, 53–63 (2011).
[CrossRef]

A. Artusi, F. Banterle, and D. Chetverikov, “A survey of specularity removal methods,” Comput. Graph. Forum 30, 2208–2230 (2011).
[CrossRef]

D. Xu, C. Doutre, and P. Nasiopoulos, “Correction of clipped pixels in color images,” IEEE Trans. Vis. Comput. Graph. 17, 333–344 (2011).

2010

D. Miyazaki, K. Hara, and K. Ikeuchi, “Median photometric stereo as applied to the Segonko tumulus,” Int. J. Comput. Vis. 86, 229–242 (2010).
[CrossRef]

2009

2008

H. L. Shen, H. G. Zhang, S. J. Shao, and J. H. Xin, “Chromaticity-based separation of reflection components in a single image,” Pattern Recogn. 41, 2461–2469 (2008).
[CrossRef]

2007

J. van de Weijer and T. Gevers, “Edge-based color constancy,” IEEE Trans. Image Process. 16, 2207–2214 (2007).
[CrossRef]

J. Toro and B. Funt, “A multilinear constraint on dichromatic planes for illumination estimation,” IEEE Trans. Image Process. 16, 92–97 (2007).
[CrossRef]

2005

R. T. Tan and K. Ikeuchi, “Separating reflection components of textured surfaces using a single image,” IEEE Trans. Pattern Anal. Mach. Intell. 27, 178–193 (2005).
[CrossRef]

2004

R. T. Tan, K. Nishino, and K. Ikeuchi, “Separating reflection components based on chromaticity and noise analysis,” IEEE Trans. Pattern Anal. Mach. Intell. 26, 1373–1379 (2004).
[CrossRef]

K. J. Yoon and I. S. Kweon, “Voting-based separation of diffuse and specular pixels,” Electron. Lett. 40, 1260–1261 (2004).
[CrossRef]

R. T. Tan, K. Nishino, and K. Ikeuchi, “Color constancy through inverse-intensity chromaticity space,” J. Opt. Soc. Am. A 21, 321–334 (2004).
[CrossRef]

2003

T. Gevers and H. Stokman, “Classifying color edges in video into shadow-geometry, highlight, or material transitions,” IEEE Trans. Multimedia 5, 237–243 (2003).

S. Barsky and M. Petrou, “The 4-source photometric stereo technique for three-dimensional surfaces in the presence of highlights and shadows,” IEEE Trans. Pattern Anal. Mach. Intell. 25, 1239–1252 (2003).
[CrossRef]

1997

S. K. Nayar, X. S. Fang, and T. Boult, “Separation of reflection components using color and polarization,” Int. J. Comput. Vis. 21, 163–186 (1997).
[CrossRef]

1994

1990

H. C. Lee, D. J. Breneman, and C. O. Schulte, “Modeling light reflection for computer color vision,” IEEE Trans. Pattern Anal. Mach. Intell. 12, 402–409 (1990).
[CrossRef]

1988

G. J. Klinker, S. A. Shafer, and T. Kanade, “The measurement of highlights in color images,” Int. J. Comput. Vis. 2, 7–32 (1988).
[CrossRef]

1985

S. A. Shafer, “Using color to separate reflection components,” Color Res. Appl. 10, 210–218 (1985).
[CrossRef]

Ahuja, N.

Q. Yang, S. Wang, N. Ahuja, and R. Yang, “A uniform framework for estimating illumination chromaticity, correspondence and specular reflection,” IEEE Trans. Image Process. 20, 53–63 (2011).
[CrossRef]

Q. Yang, S. Wang, and N. Ahuja, “Real-time specular highlight removal using bilateral filtering,” in Computer Vision–ECCV 2010 (Springer, 2010), pp. 87–100.

Artusi, A.

A. Artusi, F. Banterle, and D. Chetverikov, “A survey of specularity removal methods,” Comput. Graph. Forum 30, 2208–2230 (2011).
[CrossRef]

Banterle, F.

A. Artusi, F. Banterle, and D. Chetverikov, “A survey of specularity removal methods,” Comput. Graph. Forum 30, 2208–2230 (2011).
[CrossRef]

Barsky, S.

S. Barsky and M. Petrou, “The 4-source photometric stereo technique for three-dimensional surfaces in the presence of highlights and shadows,” IEEE Trans. Pattern Anal. Mach. Intell. 25, 1239–1252 (2003).
[CrossRef]

Belhumeur, P. N.

S. P. Mallick, T. E. Zickler, P. N. Belhumeur, and D. J. Kriegman, “Specularity removal in images and videos: a PDE approach,” in Computer Vision—ECCV 2006 (Springer, 2006), Vol. 1, pp. 550–563.

S. P. Mallick, T. E. Zickler, D. J. Kriegman, and P. N. Belhumeur, “Beyond Lambert: reconstructing specular surfaces using color,” in IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005. CVPR 2005 (IEEE Computer Society, 2005), Vol. 2, pp. 619–626.

Boult, T.

S. K. Nayar, X. S. Fang, and T. Boult, “Separation of reflection components using color and polarization,” Int. J. Comput. Vis. 21, 163–186 (1997).
[CrossRef]

Breneman, D. J.

H. C. Lee, D. J. Breneman, and C. O. Schulte, “Modeling light reflection for computer color vision,” IEEE Trans. Pattern Anal. Mach. Intell. 12, 402–409 (1990).
[CrossRef]

Cai, Q. Y.

Chetverikov, D.

A. Artusi, F. Banterle, and D. Chetverikov, “A survey of specularity removal methods,” Comput. Graph. Forum 30, 2208–2230 (2011).
[CrossRef]

Doutre, C.

D. Xu, C. Doutre, and P. Nasiopoulos, “Correction of clipped pixels in color images,” IEEE Trans. Vis. Comput. Graph. 17, 333–344 (2011).

Fang, X. S.

S. K. Nayar, X. S. Fang, and T. Boult, “Separation of reflection components using color and polarization,” Int. J. Comput. Vis. 21, 163–186 (1997).
[CrossRef]

Funt, B.

J. Toro and B. Funt, “A multilinear constraint on dichromatic planes for illumination estimation,” IEEE Trans. Image Process. 16, 92–97 (2007).
[CrossRef]

Gevers, T.

J. van de Weijer and T. Gevers, “Edge-based color constancy,” IEEE Trans. Image Process. 16, 2207–2214 (2007).
[CrossRef]

T. Gevers and H. Stokman, “Classifying color edges in video into shadow-geometry, highlight, or material transitions,” IEEE Trans. Multimedia 5, 237–243 (2003).

Hara, K.

D. Miyazaki, K. Hara, and K. Ikeuchi, “Median photometric stereo as applied to the Segonko tumulus,” Int. J. Comput. Vis. 86, 229–242 (2010).
[CrossRef]

Ikeuchi, K.

D. Miyazaki, K. Hara, and K. Ikeuchi, “Median photometric stereo as applied to the Segonko tumulus,” Int. J. Comput. Vis. 86, 229–242 (2010).
[CrossRef]

R. T. Tan and K. Ikeuchi, “Separating reflection components of textured surfaces using a single image,” IEEE Trans. Pattern Anal. Mach. Intell. 27, 178–193 (2005).
[CrossRef]

R. T. Tan, K. Nishino, and K. Ikeuchi, “Separating reflection components based on chromaticity and noise analysis,” IEEE Trans. Pattern Anal. Mach. Intell. 26, 1373–1379 (2004).
[CrossRef]

R. T. Tan, K. Nishino, and K. Ikeuchi, “Color constancy through inverse-intensity chromaticity space,” J. Opt. Soc. Am. A 21, 321–334 (2004).
[CrossRef]

Y. Sato and K. Ikeuchi, “Temporal-color space analysis of reflection,” J. Opt. Soc. Am. A 11, 2990–3002 (1994).
[CrossRef]

R. T. Tan and K. Ikeuchi, “Reflection components decomposition of texured surfaces using linear basis functions,” in IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005. CVPR 2005 (IEEE Computer Society, 2005), Vol. 1, pp. 125–131.

Kanade, T.

G. J. Klinker, S. A. Shafer, and T. Kanade, “The measurement of highlights in color images,” Int. J. Comput. Vis. 2, 7–32 (1988).
[CrossRef]

Kang, S. B.

S. Lin, Y. Li, S. B. Kang, X. Tong, and H.-Y. Shum, “Diffuse-specular separation and depth recovery from image sequences,” in Computer Vision—ECCV 2002 (Springer, 2002), pp. 210–224.

Klinker, G. J.

G. J. Klinker, S. A. Shafer, and T. Kanade, “The measurement of highlights in color images,” Int. J. Comput. Vis. 2, 7–32 (1988).
[CrossRef]

Kriegman, D. J.

S. P. Mallick, T. E. Zickler, D. J. Kriegman, and P. N. Belhumeur, “Beyond Lambert: reconstructing specular surfaces using color,” in IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005. CVPR 2005 (IEEE Computer Society, 2005), Vol. 2, pp. 619–626.

S. P. Mallick, T. E. Zickler, P. N. Belhumeur, and D. J. Kriegman, “Specularity removal in images and videos: a PDE approach,” in Computer Vision—ECCV 2006 (Springer, 2006), Vol. 1, pp. 550–563.

Kweon, I. S.

K. J. Yoon and I. S. Kweon, “Voting-based separation of diffuse and specular pixels,” Electron. Lett. 40, 1260–1261 (2004).
[CrossRef]

Lee, H. C.

H. C. Lee, D. J. Breneman, and C. O. Schulte, “Modeling light reflection for computer color vision,” IEEE Trans. Pattern Anal. Mach. Intell. 12, 402–409 (1990).
[CrossRef]

Li, Y.

S. Lin, Y. Li, S. B. Kang, X. Tong, and H.-Y. Shum, “Diffuse-specular separation and depth recovery from image sequences,” in Computer Vision—ECCV 2002 (Springer, 2002), pp. 210–224.

Lin, S.

S. Lin, Y. Li, S. B. Kang, X. Tong, and H.-Y. Shum, “Diffuse-specular separation and depth recovery from image sequences,” in Computer Vision—ECCV 2002 (Springer, 2002), pp. 210–224.

P. Tan, S. Lin, L. Quan, and H. Y. Shum, “Highlight removal by illumination-constrained inpainting,” in Ninth IEEE International Conference on Computer Vision (IEEE, 2003), Vol. 1, pp. 164–169.

Mallick, S. P.

S. P. Mallick, T. E. Zickler, P. N. Belhumeur, and D. J. Kriegman, “Specularity removal in images and videos: a PDE approach,” in Computer Vision—ECCV 2006 (Springer, 2006), Vol. 1, pp. 550–563.

S. P. Mallick, T. E. Zickler, D. J. Kriegman, and P. N. Belhumeur, “Beyond Lambert: reconstructing specular surfaces using color,” in IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005. CVPR 2005 (IEEE Computer Society, 2005), Vol. 2, pp. 619–626.

Miyazaki, D.

D. Miyazaki, K. Hara, and K. Ikeuchi, “Median photometric stereo as applied to the Segonko tumulus,” Int. J. Comput. Vis. 86, 229–242 (2010).
[CrossRef]

Nasiopoulos, P.

D. Xu, C. Doutre, and P. Nasiopoulos, “Correction of clipped pixels in color images,” IEEE Trans. Vis. Comput. Graph. 17, 333–344 (2011).

Nayar, S. K.

S. K. Nayar, X. S. Fang, and T. Boult, “Separation of reflection components using color and polarization,” Int. J. Comput. Vis. 21, 163–186 (1997).
[CrossRef]

Nishino, K.

R. T. Tan, K. Nishino, and K. Ikeuchi, “Color constancy through inverse-intensity chromaticity space,” J. Opt. Soc. Am. A 21, 321–334 (2004).
[CrossRef]

R. T. Tan, K. Nishino, and K. Ikeuchi, “Separating reflection components based on chromaticity and noise analysis,” IEEE Trans. Pattern Anal. Mach. Intell. 26, 1373–1379 (2004).
[CrossRef]

Petrou, M.

S. Barsky and M. Petrou, “The 4-source photometric stereo technique for three-dimensional surfaces in the presence of highlights and shadows,” IEEE Trans. Pattern Anal. Mach. Intell. 25, 1239–1252 (2003).
[CrossRef]

Quan, L.

P. Tan, S. Lin, L. Quan, and H. Y. Shum, “Highlight removal by illumination-constrained inpainting,” in Ninth IEEE International Conference on Computer Vision (IEEE, 2003), Vol. 1, pp. 164–169.

Sato, Y.

Schulte, C. O.

H. C. Lee, D. J. Breneman, and C. O. Schulte, “Modeling light reflection for computer color vision,” IEEE Trans. Pattern Anal. Mach. Intell. 12, 402–409 (1990).
[CrossRef]

Shafer, S. A.

G. J. Klinker, S. A. Shafer, and T. Kanade, “The measurement of highlights in color images,” Int. J. Comput. Vis. 2, 7–32 (1988).
[CrossRef]

S. A. Shafer, “Using color to separate reflection components,” Color Res. Appl. 10, 210–218 (1985).
[CrossRef]

Shao, S. J.

H. L. Shen, H. G. Zhang, S. J. Shao, and J. H. Xin, “Chromaticity-based separation of reflection components in a single image,” Pattern Recogn. 41, 2461–2469 (2008).
[CrossRef]

Shen, H. L.

H. L. Shen and Q. Y. Cai, “Simple and efficient method for specularity removal in an image,” Appl. Opt. 48, 2711–2719 (2009).
[CrossRef]

H. L. Shen, H. G. Zhang, S. J. Shao, and J. H. Xin, “Chromaticity-based separation of reflection components in a single image,” Pattern Recogn. 41, 2461–2469 (2008).
[CrossRef]

Shum, H. Y.

P. Tan, S. Lin, L. Quan, and H. Y. Shum, “Highlight removal by illumination-constrained inpainting,” in Ninth IEEE International Conference on Computer Vision (IEEE, 2003), Vol. 1, pp. 164–169.

Shum, H.-Y.

S. Lin, Y. Li, S. B. Kang, X. Tong, and H.-Y. Shum, “Diffuse-specular separation and depth recovery from image sequences,” in Computer Vision—ECCV 2002 (Springer, 2002), pp. 210–224.

Stokman, H.

T. Gevers and H. Stokman, “Classifying color edges in video into shadow-geometry, highlight, or material transitions,” IEEE Trans. Multimedia 5, 237–243 (2003).

Tan, P.

P. Tan, S. Lin, L. Quan, and H. Y. Shum, “Highlight removal by illumination-constrained inpainting,” in Ninth IEEE International Conference on Computer Vision (IEEE, 2003), Vol. 1, pp. 164–169.

Tan, R. T.

R. T. Tan and K. Ikeuchi, “Separating reflection components of textured surfaces using a single image,” IEEE Trans. Pattern Anal. Mach. Intell. 27, 178–193 (2005).
[CrossRef]

R. T. Tan, K. Nishino, and K. Ikeuchi, “Separating reflection components based on chromaticity and noise analysis,” IEEE Trans. Pattern Anal. Mach. Intell. 26, 1373–1379 (2004).
[CrossRef]

R. T. Tan, K. Nishino, and K. Ikeuchi, “Color constancy through inverse-intensity chromaticity space,” J. Opt. Soc. Am. A 21, 321–334 (2004).
[CrossRef]

R. T. Tan and K. Ikeuchi, “Reflection components decomposition of texured surfaces using linear basis functions,” in IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005. CVPR 2005 (IEEE Computer Society, 2005), Vol. 1, pp. 125–131.

Tong, X.

S. Lin, Y. Li, S. B. Kang, X. Tong, and H.-Y. Shum, “Diffuse-specular separation and depth recovery from image sequences,” in Computer Vision—ECCV 2002 (Springer, 2002), pp. 210–224.

Toro, J.

J. Toro and B. Funt, “A multilinear constraint on dichromatic planes for illumination estimation,” IEEE Trans. Image Process. 16, 92–97 (2007).
[CrossRef]

van de Weijer, J.

J. van de Weijer and T. Gevers, “Edge-based color constancy,” IEEE Trans. Image Process. 16, 2207–2214 (2007).
[CrossRef]

Wang, S.

Q. Yang, S. Wang, N. Ahuja, and R. Yang, “A uniform framework for estimating illumination chromaticity, correspondence and specular reflection,” IEEE Trans. Image Process. 20, 53–63 (2011).
[CrossRef]

Q. Yang, S. Wang, and N. Ahuja, “Real-time specular highlight removal using bilateral filtering,” in Computer Vision–ECCV 2010 (Springer, 2010), pp. 87–100.

Xin, J. H.

H. L. Shen, H. G. Zhang, S. J. Shao, and J. H. Xin, “Chromaticity-based separation of reflection components in a single image,” Pattern Recogn. 41, 2461–2469 (2008).
[CrossRef]

Xu, D.

D. Xu, C. Doutre, and P. Nasiopoulos, “Correction of clipped pixels in color images,” IEEE Trans. Vis. Comput. Graph. 17, 333–344 (2011).

Yang, Q.

Q. Yang, S. Wang, N. Ahuja, and R. Yang, “A uniform framework for estimating illumination chromaticity, correspondence and specular reflection,” IEEE Trans. Image Process. 20, 53–63 (2011).
[CrossRef]

Q. Yang, S. Wang, and N. Ahuja, “Real-time specular highlight removal using bilateral filtering,” in Computer Vision–ECCV 2010 (Springer, 2010), pp. 87–100.

Yang, R.

Q. Yang, S. Wang, N. Ahuja, and R. Yang, “A uniform framework for estimating illumination chromaticity, correspondence and specular reflection,” IEEE Trans. Image Process. 20, 53–63 (2011).
[CrossRef]

Yoon, K. J.

K. J. Yoon and I. S. Kweon, “Voting-based separation of diffuse and specular pixels,” Electron. Lett. 40, 1260–1261 (2004).
[CrossRef]

Zhang, H. G.

H. L. Shen, H. G. Zhang, S. J. Shao, and J. H. Xin, “Chromaticity-based separation of reflection components in a single image,” Pattern Recogn. 41, 2461–2469 (2008).
[CrossRef]

Zickler, T. E.

S. P. Mallick, T. E. Zickler, P. N. Belhumeur, and D. J. Kriegman, “Specularity removal in images and videos: a PDE approach,” in Computer Vision—ECCV 2006 (Springer, 2006), Vol. 1, pp. 550–563.

S. P. Mallick, T. E. Zickler, D. J. Kriegman, and P. N. Belhumeur, “Beyond Lambert: reconstructing specular surfaces using color,” in IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005. CVPR 2005 (IEEE Computer Society, 2005), Vol. 2, pp. 619–626.

Appl. Opt.

Color Res. Appl.

S. A. Shafer, “Using color to separate reflection components,” Color Res. Appl. 10, 210–218 (1985).
[CrossRef]

Comput. Graph. Forum

A. Artusi, F. Banterle, and D. Chetverikov, “A survey of specularity removal methods,” Comput. Graph. Forum 30, 2208–2230 (2011).
[CrossRef]

Electron. Lett.

K. J. Yoon and I. S. Kweon, “Voting-based separation of diffuse and specular pixels,” Electron. Lett. 40, 1260–1261 (2004).
[CrossRef]

IEEE Trans. Image Process.

J. van de Weijer and T. Gevers, “Edge-based color constancy,” IEEE Trans. Image Process. 16, 2207–2214 (2007).
[CrossRef]

J. Toro and B. Funt, “A multilinear constraint on dichromatic planes for illumination estimation,” IEEE Trans. Image Process. 16, 92–97 (2007).
[CrossRef]

Q. Yang, S. Wang, N. Ahuja, and R. Yang, “A uniform framework for estimating illumination chromaticity, correspondence and specular reflection,” IEEE Trans. Image Process. 20, 53–63 (2011).
[CrossRef]

IEEE Trans. Multimedia

T. Gevers and H. Stokman, “Classifying color edges in video into shadow-geometry, highlight, or material transitions,” IEEE Trans. Multimedia 5, 237–243 (2003).

IEEE Trans. Pattern Anal. Mach. Intell.

H. C. Lee, D. J. Breneman, and C. O. Schulte, “Modeling light reflection for computer color vision,” IEEE Trans. Pattern Anal. Mach. Intell. 12, 402–409 (1990).
[CrossRef]

R. T. Tan, K. Nishino, and K. Ikeuchi, “Separating reflection components based on chromaticity and noise analysis,” IEEE Trans. Pattern Anal. Mach. Intell. 26, 1373–1379 (2004).
[CrossRef]

R. T. Tan and K. Ikeuchi, “Separating reflection components of textured surfaces using a single image,” IEEE Trans. Pattern Anal. Mach. Intell. 27, 178–193 (2005).
[CrossRef]

S. Barsky and M. Petrou, “The 4-source photometric stereo technique for three-dimensional surfaces in the presence of highlights and shadows,” IEEE Trans. Pattern Anal. Mach. Intell. 25, 1239–1252 (2003).
[CrossRef]

IEEE Trans. Vis. Comput. Graph.

D. Xu, C. Doutre, and P. Nasiopoulos, “Correction of clipped pixels in color images,” IEEE Trans. Vis. Comput. Graph. 17, 333–344 (2011).

Int. J. Comput. Vis.

S. K. Nayar, X. S. Fang, and T. Boult, “Separation of reflection components using color and polarization,” Int. J. Comput. Vis. 21, 163–186 (1997).
[CrossRef]

D. Miyazaki, K. Hara, and K. Ikeuchi, “Median photometric stereo as applied to the Segonko tumulus,” Int. J. Comput. Vis. 86, 229–242 (2010).
[CrossRef]

G. J. Klinker, S. A. Shafer, and T. Kanade, “The measurement of highlights in color images,” Int. J. Comput. Vis. 2, 7–32 (1988).
[CrossRef]

J. Opt. Soc. Am. A

Pattern Recogn.

H. L. Shen, H. G. Zhang, S. J. Shao, and J. H. Xin, “Chromaticity-based separation of reflection components in a single image,” Pattern Recogn. 41, 2461–2469 (2008).
[CrossRef]

Other

Q. Yang, S. Wang, and N. Ahuja, “Real-time specular highlight removal using bilateral filtering,” in Computer Vision–ECCV 2010 (Springer, 2010), pp. 87–100.

R. T. Tan and K. Ikeuchi, “Reflection components decomposition of texured surfaces using linear basis functions,” in IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005. CVPR 2005 (IEEE Computer Society, 2005), Vol. 1, pp. 125–131.

S. Lin, Y. Li, S. B. Kang, X. Tong, and H.-Y. Shum, “Diffuse-specular separation and depth recovery from image sequences,” in Computer Vision—ECCV 2002 (Springer, 2002), pp. 210–224.

P. Tan, S. Lin, L. Quan, and H. Y. Shum, “Highlight removal by illumination-constrained inpainting,” in Ninth IEEE International Conference on Computer Vision (IEEE, 2003), Vol. 1, pp. 164–169.

S. P. Mallick, T. E. Zickler, P. N. Belhumeur, and D. J. Kriegman, “Specularity removal in images and videos: a PDE approach,” in Computer Vision—ECCV 2006 (Springer, 2006), Vol. 1, pp. 550–563.

S. P. Mallick, T. E. Zickler, D. J. Kriegman, and P. N. Belhumeur, “Beyond Lambert: reconstructing specular surfaces using color,” in IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005. CVPR 2005 (IEEE Computer Society, 2005), Vol. 2, pp. 619–626.

R. T. Tan, “Specular highlight removal from a single image,” http://people.cs.uu.nl/robby/code.html .

Q. Yang, “Real-time specular highlight removal using bilateral filtering,” http://www.cs.cityu.edu.hk/~qiyang/ .

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Figures (12)

Fig. 1.
Fig. 1.

Illustration of intensity ratio on a real object. (a) Object image, (b) intensity ratios along the marked image row.

Fig. 2.
Fig. 2.

Clusters and corresponding intensity ratios of the Fish image. (a) Original image and (b) ratio map obtained using thresholds TP=0.2 and TC=0.2. This threshold setting results in four clusters, with estimated intensity ratios Q^d=1.93, 1.37, 1.26, and 1.22. The ratio map is enhanced for visualization.

Fig. 3.
Fig. 3.

Distributions of global PSNR values with respect to the chromaticity threshold TC and percentile threshold TP for the images.

Fig. 4.
Fig. 4.

Separated diffuse reflection components of the Masks image using different methods. The global and regional PSNR values are, respectively, shown in the parenthesis.

Fig. 5.
Fig. 5.

Separated diffuse reflection components of the Cups image using different methods. The global and regional PSNR values are, respectively, shown in the parenthesis.

Fig. 6.
Fig. 6.

Separated diffuse reflection components of the Fruit image using different methods. The global and regional PSNR values are, respectively, shown in the parenthesis.

Fig. 7.
Fig. 7.

Separated diffuse reflection components of the Animals image using different methods. The global and regional PSNR values are, respectively, shown in the parenthesis.

Fig. 8.
Fig. 8.

Separated diffuse reflection components of the Watermelon image using different methods.

Fig. 9.
Fig. 9.

Separated diffuse reflection components of the Rabbit image using different methods. The inset in (e) indicates a limitation of the proposed method in a dark boundary area.

Fig. 10.
Fig. 10.

Separated diffuse reflection components of the Wood image using different methods. The masked area in (e) indicates a limitation of the proposed method on a white surface.

Fig. 11.
Fig. 11.

Separated diffuse reflection components of the Fish image using different methods.

Fig. 12.
Fig. 12.

Separated diffuse reflection components of the Toys image using different methods.

Tables (3)

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Algorithm 1 Pixel clustering

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Table 1. Global and Regional PSNR Values of Reflection Separation Methods When Images are Corrupted by Additive Gaussian Noise with Various Noise Levels (σ)a

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Table 2. Computational Time (in seconds) of Yang’s Method [19] and the Proposed Method on a Personal Computer

Equations (16)

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I(x)=ID(x)+IS(x)=md(x)Λ+ms(x)Γ,
Imin(x)=min{Ir(x),Ig(x),Ib(x)}=md(x)Λmin+ms(x)Γ,
Imax(x)=md(x)Λmax+ms(x)Γ,
Iran(x)=Imax(x)Imin(x)=md(x)(ΛmaxΛmin),
Q(x)=Imax(x)Iran(x).
Qd(x)=ΛmaxΛmaxΛmin,
Qs(x)=ΛmaxΛmaxΛmin+ms(x)Γmd(x)(ΛmaxΛmin),
Q^d=Q(x^).
Q^d=medianxΩ{Q(x)},
IS(x)=max{Imax(x)Q^dIran,0},
ID(x)=I(x)IS(x),
IcSF(x)=Ic(x)Imin(x)+I¯min,
I¯min=x=1NImin(x)N,
ΛSF(x)=ISF(x)IrSF(x)+IgSF(x)+IbSF(x).
ΛP(x)=[ΛminSF(x),ΛmaxSF(x)]T,
dΛ(x,y)=ΛP(x)ΛP(y)1.

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