Abstract

A grey pixel detection method, based on the illuminant-invariant descriptor in three logarithmic color channels, was proposed and proved effective for illuminant estimation [25]. In this work, an incremental improvement measure, based on Yang’s work, is proposed by redefining the illuminant-invariant descriptor and the grey pixels. In order to improve the accuracy, appropriate parameters of grey pixels ratio and filter size are statistically analyzed and selected on different data sets. Experimental results show that the improved method is not only effective compared with classical statistics-based methods, but also outperforms many learning-based methods.

© 2018 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

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2018 (1)

C. Tang, X. W. Liu, M. M. Li, P. C. Wang, J. J. Chen, L. Z. Wang, and W. Q. Li, “Robust Unsupervised Feature Selection via Dual Self-representation and Manifold Regularization,” Knowl-based. Syst. 145, 109–120 (2018).

2017 (5)

C. Tang, P. C. Wang, C. Q. Zhang, and W. Q. Li, “Salient Object Detection via Weighted Low Rank Matrix Recovery,” IEEE Signal Process. Lett. 24(4), 490–494 (2017).
[Crossref]

H. A. Khan, J. B. Thomas, J. Y. Hardeberg, and O. Laligant, “Illuminant estimation in multispectral imaging,” J. Opt. Soc. Am. A 34(7), 1085–1098 (2017).
[Crossref] [PubMed]

S. B. Gao, M. Zhang, C. Y. Li, and Y. J. Li, “Improving color constancy by discounting the variation of camera spectral sensitivity,” J. Opt. Soc. Am. A 34(8), 1448–1462 (2017).
[Crossref] [PubMed]

S. Bianco, C. Cusano, and R. Schettini, “Single and multiple illuminant estimation using convolutional neural networks,” IEEE Trans. Image Process. 26(9), 4347–4362 (2017).
[Crossref] [PubMed]

S. W. Oh and S. J. Kim, “Approaching the computational color constancy as a classification problem through deep learning,” Pattern Recognit. 61, 405–416 (2017).
[Crossref]

2016 (1)

B. Li, W. Xiong, W. Hu, B. Funt, and J. Xing, “Multi-cue illumination estimation via a tree-structured group joint sparse representation,” Int. J. Comput. Vis. 117(1), 21–47 (2016).
[Crossref]

2015 (3)

D. An, J. Suo, H. Wang, and Q. Dai, “Illumination estimation from specular highlight in a multi-spectral image,” Opt. Express 23(13), 17008–17023 (2015).
[Crossref] [PubMed]

C. Barata, M. E. Celebi, and J. S. Marques, “Improving dermoscopy image classification using color constancy,” IEEE J. Biomed. Health Inform. 19(3), 1146–1152 (2015).
[PubMed]

M. R. Luo, G. Cui, and M. Georgoula, “Colour difference evaluation for white light sources,” Light. Res. Technol. 47(3), 360–369 (2015).
[Crossref]

2014 (4)

Y. Ohno and P. Blattner, “Chromaticity difference specification for light sources,” International Commission on Illumination, Tech. Rep. CIE TN 001, 2014 (2014).

H. R. V. Joze and M. S. Drew, “Exemplar-Based Color Constancy and Multiple Illumination,” IEEE Trans. Pattern Anal. Mach. Intell. 36(5), 860–873 (2014).
[Crossref] [PubMed]

Bing Li, Weihua Xiong, Weiming Hu, and B. Funt, “Evaluating combinational illumination estimation methods on real-world images,” IEEE Trans. Image Process. 23(3), 1194–1209 (2014).
[Crossref] [PubMed]

D. Cheng, D. K. Prasad, and M. S. Brown, “Illuminant estimation for color constancy: why spatial-domain methods work and the role of the color distribution,” J. Opt. Soc. Am. A 31(5), 1049–1058 (2014).
[Crossref] [PubMed]

2012 (2)

A. Gijsenij, T. Gevers, and J. van de Weijer, “Improving color constancy by photometric edge weighting,” IEEE Trans. Pattern Anal. Mach. Intell. 34(5), 918–929 (2012).
[Crossref] [PubMed]

A. Chakrabarti, K. Hirakawa, and T. Zickler, “Color constancy with spatio-spectral statistics,” IEEE Trans. Pattern Anal. Mach. Intell. 34(8), 1509–1519 (2012).
[Crossref] [PubMed]

2011 (3)

A. Gijsenij and T. Gevers, “Color constancy using natural image statistics and scene semantics,” IEEE Trans. Pattern Anal. Mach. Intell. 33(4), 687–698 (2011).
[Crossref] [PubMed]

L. Shi, W. Xiong, and B. Funt, “Illumination estimation via thin-plate spline interpolation,” J. Opt. Soc. Am. A 28(5), 940–948 (2011).
[Crossref] [PubMed]

A. Gijsenij, T. Gevers, and J. van de Weijer, “Computational color constancy: Survey and experiments,” IEEE Trans. Image Process. 20(9), 2475–2489 (2011).
[Crossref] [PubMed]

2010 (4)

W. T. Chen, W. C. Liu, and M. S. Chen, “Adaptive color feature extraction based on image color distributions,” IEEE Trans. Image Process. 19(8), 2005–2016 (2010).
[Crossref] [PubMed]

S. Bianco, G. Ciocca, C. Cusano, and R. Schettini, “Automatic color constancy algorithm selection and combination,” Pattern Recognit. 43(3), 695–705 (2010).
[Crossref]

A. Gijsenij, T. Gevers, and J. van De Weijer, “Generalized gamut mapping using image derivative structures for color constancy,” Int. J. Comput. Vis. 86(2–3), 127–139 (2010).
[Crossref]

B. Li, D. Xu, W. Xiong, and S. Feng, “Color constancy using achromatic surface,” Color Res. Appl. 35(4), 304–332 (2010).
[Crossref]

2008 (1)

S. Bianco, G. Ciocca, C. Cusano, and R. Schettini, “Improving color constancy using indoor-outdoor image classification,” IEEE Trans. Image Process. 17(12), 2381–2392 (2008).
[Crossref] [PubMed]

2007 (1)

J. van de Weijer, T. Gevers, and A. Gijsenij, “Edge-based color constancy,” IEEE Trans. Image Process. 16(9), 2207–2214 (2007).
[Crossref] [PubMed]

2006 (1)

S. D. Hordley, “Scene illuminant estimation: past, present, and future,” Color Res. Appl. 31(4), 303–314 (2006).
[Crossref]

2004 (1)

2002 (1)

K. Barnard, V. Cardei, and B. Funt, “A comparison of computational color constancy algorithms--part I: methodology and experiments with synthesized data,” IEEE Trans. Image Process. 11(9), 972–983 (2002).
[Crossref] [PubMed]

1986 (2)

1980 (1)

G. Buchsbaum, “A spatial processor model for object colour perception,” J. Franklin Inst. 310(1), 1–261 (1980).
[Crossref]

1977 (1)

E. H. Land, “The retinex theory of color vision,” Sci. Am. 237(6), 108–128 (1977).
[Crossref] [PubMed]

An, D.

Barata, C.

C. Barata, M. E. Celebi, and J. S. Marques, “Improving dermoscopy image classification using color constancy,” IEEE J. Biomed. Health Inform. 19(3), 1146–1152 (2015).
[PubMed]

Barnard, K.

K. Barnard, V. Cardei, and B. Funt, “A comparison of computational color constancy algorithms--part I: methodology and experiments with synthesized data,” IEEE Trans. Image Process. 11(9), 972–983 (2002).
[Crossref] [PubMed]

Barron, J. T.

J. T. Barron and Y. T. Tsai, “Fast fourier color constancy,” in IEEE Conference on Computer Vision and Pattern Recognition (2017), pp. 21–26.

Bianco, S.

S. Bianco, C. Cusano, and R. Schettini, “Single and multiple illuminant estimation using convolutional neural networks,” IEEE Trans. Image Process. 26(9), 4347–4362 (2017).
[Crossref] [PubMed]

S. Bianco, G. Ciocca, C. Cusano, and R. Schettini, “Automatic color constancy algorithm selection and combination,” Pattern Recognit. 43(3), 695–705 (2010).
[Crossref]

S. Bianco, G. Ciocca, C. Cusano, and R. Schettini, “Improving color constancy using indoor-outdoor image classification,” IEEE Trans. Image Process. 17(12), 2381–2392 (2008).
[Crossref] [PubMed]

S. Bianco, C. Cusano, and R. Schettini, “Color constancy using CNNs,” in IEEE Conference on Computer Vision and Pattern Recognition (2015), pp. 81–89.

Bing Li,

Bing Li, Weihua Xiong, Weiming Hu, and B. Funt, “Evaluating combinational illumination estimation methods on real-world images,” IEEE Trans. Image Process. 23(3), 1194–1209 (2014).
[Crossref] [PubMed]

Blake, A.

P. V. Gehler, C. Rother, A. Blake, T. Minka, and T. Sharp, “Bayesian color constancy revisited,” in IEEE Conference on Computer Vision and Pattern Recognition (2008), pp. 1–8.

Blattner, P.

Y. Ohno and P. Blattner, “Chromaticity difference specification for light sources,” International Commission on Illumination, Tech. Rep. CIE TN 001, 2014 (2014).

Brainard, D. H.

Brown, M. S.

D. Cheng, D. K. Prasad, and M. S. Brown, “Illuminant estimation for color constancy: why spatial-domain methods work and the role of the color distribution,” J. Opt. Soc. Am. A 31(5), 1049–1058 (2014).
[Crossref] [PubMed]

Y. Li, R. T. Tan, and M. S. Brown, “Nighttime haze removal with glow and multiple light colors,” in IEEE International Conference on Computer Vision (2015), pp. 226–234.

D. Cheng, B. Price, S. Cohen, and M. S. Brown, “Effective learning-based illuminant estimation using simple features,” in IEEE Conference on Computer Vision and Pattern Recognition (2015), pp. 1000–1008.

Buchsbaum, G.

G. Buchsbaum, “A spatial processor model for object colour perception,” J. Franklin Inst. 310(1), 1–261 (1980).
[Crossref]

Cardei, V.

K. Barnard, V. Cardei, and B. Funt, “A comparison of computational color constancy algorithms--part I: methodology and experiments with synthesized data,” IEEE Trans. Image Process. 11(9), 972–983 (2002).
[Crossref] [PubMed]

Celebi, M. E.

C. Barata, M. E. Celebi, and J. S. Marques, “Improving dermoscopy image classification using color constancy,” IEEE J. Biomed. Health Inform. 19(3), 1146–1152 (2015).
[PubMed]

Chakrabarti, A.

A. Chakrabarti, K. Hirakawa, and T. Zickler, “Color constancy with spatio-spectral statistics,” IEEE Trans. Pattern Anal. Mach. Intell. 34(8), 1509–1519 (2012).
[Crossref] [PubMed]

Chen, J. J.

C. Tang, X. W. Liu, M. M. Li, P. C. Wang, J. J. Chen, L. Z. Wang, and W. Q. Li, “Robust Unsupervised Feature Selection via Dual Self-representation and Manifold Regularization,” Knowl-based. Syst. 145, 109–120 (2018).

Chen, K.

Y. Qian, K. Chen, J. Nikkanen, J. K. Kamarainen, and J. Matas, “Recurrent Color Constancy,” in IEEE International Conference on Computer Vision (2017), pp. 5459–5467.

Chen, M. S.

W. T. Chen, W. C. Liu, and M. S. Chen, “Adaptive color feature extraction based on image color distributions,” IEEE Trans. Image Process. 19(8), 2005–2016 (2010).
[Crossref] [PubMed]

Chen, W. T.

W. T. Chen, W. C. Liu, and M. S. Chen, “Adaptive color feature extraction based on image color distributions,” IEEE Trans. Image Process. 19(8), 2005–2016 (2010).
[Crossref] [PubMed]

Cheng, D.

D. Cheng, D. K. Prasad, and M. S. Brown, “Illuminant estimation for color constancy: why spatial-domain methods work and the role of the color distribution,” J. Opt. Soc. Am. A 31(5), 1049–1058 (2014).
[Crossref] [PubMed]

D. Cheng, B. Price, S. Cohen, and M. S. Brown, “Effective learning-based illuminant estimation using simple features,” in IEEE Conference on Computer Vision and Pattern Recognition (2015), pp. 1000–1008.

Ciocca, G.

S. Bianco, G. Ciocca, C. Cusano, and R. Schettini, “Automatic color constancy algorithm selection and combination,” Pattern Recognit. 43(3), 695–705 (2010).
[Crossref]

S. Bianco, G. Ciocca, C. Cusano, and R. Schettini, “Improving color constancy using indoor-outdoor image classification,” IEEE Trans. Image Process. 17(12), 2381–2392 (2008).
[Crossref] [PubMed]

Ciurea, F.

F. Ciurea and B. Funt, “A Large Image Database for Color Constancy Research,” in Proceedings of the Imaging Science and Technology Eleventh Color Imaging Conference (2003), pp. 160–164.

Cohen, S.

D. Cheng, B. Price, S. Cohen, and M. S. Brown, “Effective learning-based illuminant estimation using simple features,” in IEEE Conference on Computer Vision and Pattern Recognition (2015), pp. 1000–1008.

Cui, G.

M. R. Luo, G. Cui, and M. Georgoula, “Colour difference evaluation for white light sources,” Light. Res. Technol. 47(3), 360–369 (2015).
[Crossref]

Cusano, C.

S. Bianco, C. Cusano, and R. Schettini, “Single and multiple illuminant estimation using convolutional neural networks,” IEEE Trans. Image Process. 26(9), 4347–4362 (2017).
[Crossref] [PubMed]

S. Bianco, G. Ciocca, C. Cusano, and R. Schettini, “Automatic color constancy algorithm selection and combination,” Pattern Recognit. 43(3), 695–705 (2010).
[Crossref]

S. Bianco, G. Ciocca, C. Cusano, and R. Schettini, “Improving color constancy using indoor-outdoor image classification,” IEEE Trans. Image Process. 17(12), 2381–2392 (2008).
[Crossref] [PubMed]

S. Bianco, C. Cusano, and R. Schettini, “Color constancy using CNNs,” in IEEE Conference on Computer Vision and Pattern Recognition (2015), pp. 81–89.

Dai, Q.

Danelljan, M.

M. Danelljan, F. S. Khan, M. Felsberg, and J. van de Weijer, “Adaptive color attributes for real-time visual tracking,” in IEEE Conference on Computer Vision and Pattern Recognition (2014), pp. 1090–1097.

Drew, M. S.

H. R. V. Joze and M. S. Drew, “Exemplar-Based Color Constancy and Multiple Illumination,” IEEE Trans. Pattern Anal. Mach. Intell. 36(5), 860–873 (2014).
[Crossref] [PubMed]

H. R. V. Joze, M. S. Drew, G. D. Finlayson, and P. A. T. Rey, “The role of bright pixels in illumination estimation,” in Color and Imaging Conference (2012), pp. 41–46.

Felsberg, M.

M. Danelljan, F. S. Khan, M. Felsberg, and J. van de Weijer, “Adaptive color attributes for real-time visual tracking,” in IEEE Conference on Computer Vision and Pattern Recognition (2014), pp. 1090–1097.

Feng, S.

B. Li, D. Xu, W. Xiong, and S. Feng, “Color constancy using achromatic surface,” Color Res. Appl. 35(4), 304–332 (2010).
[Crossref]

Finlayson, G.

G. Finlayson, “Corrected-moment illuminant estimation,” in IEEE International Conference on Computer Vision (2013), pp. 1904–1911.

Finlayson, G. D.

H. R. V. Joze, M. S. Drew, G. D. Finlayson, and P. A. T. Rey, “The role of bright pixels in illumination estimation,” in Color and Imaging Conference (2012), pp. 41–46.

G. D. Finlayson and E. Trezzi, “Shades of gray and colour constancy,” in Color and Imaging Conference (2004), pp. 37–41.

Funt, B.

B. Li, W. Xiong, W. Hu, B. Funt, and J. Xing, “Multi-cue illumination estimation via a tree-structured group joint sparse representation,” Int. J. Comput. Vis. 117(1), 21–47 (2016).
[Crossref]

Bing Li, Weihua Xiong, Weiming Hu, and B. Funt, “Evaluating combinational illumination estimation methods on real-world images,” IEEE Trans. Image Process. 23(3), 1194–1209 (2014).
[Crossref] [PubMed]

L. Shi, W. Xiong, and B. Funt, “Illumination estimation via thin-plate spline interpolation,” J. Opt. Soc. Am. A 28(5), 940–948 (2011).
[Crossref] [PubMed]

K. Barnard, V. Cardei, and B. Funt, “A comparison of computational color constancy algorithms--part I: methodology and experiments with synthesized data,” IEEE Trans. Image Process. 11(9), 972–983 (2002).
[Crossref] [PubMed]

B. Funt and M. Mosny, “Removing outliers in illumination estimation,” in Color and Imaging Conference (2012), pp. 105–110.

F. Ciurea and B. Funt, “A Large Image Database for Color Constancy Research,” in Proceedings of the Imaging Science and Technology Eleventh Color Imaging Conference (2003), pp. 160–164.

W. Xiong, B. Funt, and L. Shi, “Automatic white balancing via grey surface identification,” in Proceeding of 15th Color Imaging Conference: Color Science, Systems and Applications.Springfield (2007), pp. 5–9.

Gao, S.

K. Yang, S. Gao, C. Li, and Y. Li, “Efficient color boundary detection with color-opponent mechanisms,” in IEEE Conference on Computer Vision and Pattern Recognition (2013), pp. 2810–2817.

S. Gao, K. Yang, C. Li, and Y. Li, “A color constancy model with double-opponency mechanisms,” in IEEE International Conference on Computer Vision (2013), pp. 929–936.

S. Gao, W. Han, K. Yang, C. Li, and Y. Li, “Efficient color constancy with local surface reflectance statistics,” in European Conference on Computer Vision (2014), pp. 158–173.

Gao, S. B.

S. B. Gao, M. Zhang, C. Y. Li, and Y. J. Li, “Improving color constancy by discounting the variation of camera spectral sensitivity,” J. Opt. Soc. Am. A 34(8), 1448–1462 (2017).
[Crossref] [PubMed]

K. F. Yang, S. B. Gao, and Y. J. Li, “Efficient illuminant estimation for color constancy using grey pixels,” in IEEE Conference on Computer Vision and Pattern Recognition (2015), pp. 2254–2263.

Gehler, P. V.

P. V. Gehler, C. Rother, A. Blake, T. Minka, and T. Sharp, “Bayesian color constancy revisited,” in IEEE Conference on Computer Vision and Pattern Recognition (2008), pp. 1–8.

Georgoula, M.

M. R. Luo, G. Cui, and M. Georgoula, “Colour difference evaluation for white light sources,” Light. Res. Technol. 47(3), 360–369 (2015).
[Crossref]

Gevers, T.

A. Gijsenij, T. Gevers, and J. van de Weijer, “Improving color constancy by photometric edge weighting,” IEEE Trans. Pattern Anal. Mach. Intell. 34(5), 918–929 (2012).
[Crossref] [PubMed]

A. Gijsenij and T. Gevers, “Color constancy using natural image statistics and scene semantics,” IEEE Trans. Pattern Anal. Mach. Intell. 33(4), 687–698 (2011).
[Crossref] [PubMed]

A. Gijsenij, T. Gevers, and J. van de Weijer, “Computational color constancy: Survey and experiments,” IEEE Trans. Image Process. 20(9), 2475–2489 (2011).
[Crossref] [PubMed]

A. Gijsenij, T. Gevers, and J. van De Weijer, “Generalized gamut mapping using image derivative structures for color constancy,” Int. J. Comput. Vis. 86(2–3), 127–139 (2010).
[Crossref]

J. van de Weijer, T. Gevers, and A. Gijsenij, “Edge-based color constancy,” IEEE Trans. Image Process. 16(9), 2207–2214 (2007).
[Crossref] [PubMed]

Gijsenij, A.

A. Gijsenij, T. Gevers, and J. van de Weijer, “Improving color constancy by photometric edge weighting,” IEEE Trans. Pattern Anal. Mach. Intell. 34(5), 918–929 (2012).
[Crossref] [PubMed]

A. Gijsenij, T. Gevers, and J. van de Weijer, “Computational color constancy: Survey and experiments,” IEEE Trans. Image Process. 20(9), 2475–2489 (2011).
[Crossref] [PubMed]

A. Gijsenij and T. Gevers, “Color constancy using natural image statistics and scene semantics,” IEEE Trans. Pattern Anal. Mach. Intell. 33(4), 687–698 (2011).
[Crossref] [PubMed]

A. Gijsenij, T. Gevers, and J. van De Weijer, “Generalized gamut mapping using image derivative structures for color constancy,” Int. J. Comput. Vis. 86(2–3), 127–139 (2010).
[Crossref]

J. van de Weijer, T. Gevers, and A. Gijsenij, “Edge-based color constancy,” IEEE Trans. Image Process. 16(9), 2207–2214 (2007).
[Crossref] [PubMed]

Han, W.

S. Gao, W. Han, K. Yang, C. Li, and Y. Li, “Efficient color constancy with local surface reflectance statistics,” in European Conference on Computer Vision (2014), pp. 158–173.

Hardeberg, J. Y.

Hirakawa, K.

A. Chakrabarti, K. Hirakawa, and T. Zickler, “Color constancy with spatio-spectral statistics,” IEEE Trans. Pattern Anal. Mach. Intell. 34(8), 1509–1519 (2012).
[Crossref] [PubMed]

Hordley, S. D.

S. D. Hordley, “Scene illuminant estimation: past, present, and future,” Color Res. Appl. 31(4), 303–314 (2006).
[Crossref]

Hu, W.

B. Li, W. Xiong, W. Hu, B. Funt, and J. Xing, “Multi-cue illumination estimation via a tree-structured group joint sparse representation,” Int. J. Comput. Vis. 117(1), 21–47 (2016).
[Crossref]

Ikeuchi, K.

Joze, H. R. V.

H. R. V. Joze and M. S. Drew, “Exemplar-Based Color Constancy and Multiple Illumination,” IEEE Trans. Pattern Anal. Mach. Intell. 36(5), 860–873 (2014).
[Crossref] [PubMed]

H. R. V. Joze, M. S. Drew, G. D. Finlayson, and P. A. T. Rey, “The role of bright pixels in illumination estimation,” in Color and Imaging Conference (2012), pp. 41–46.

Kamarainen, J. K.

Y. Qian, K. Chen, J. Nikkanen, J. K. Kamarainen, and J. Matas, “Recurrent Color Constancy,” in IEEE International Conference on Computer Vision (2017), pp. 5459–5467.

Khan, F. S.

M. Danelljan, F. S. Khan, M. Felsberg, and J. van de Weijer, “Adaptive color attributes for real-time visual tracking,” in IEEE Conference on Computer Vision and Pattern Recognition (2014), pp. 1090–1097.

Khan, H. A.

Kim, S. J.

S. W. Oh and S. J. Kim, “Approaching the computational color constancy as a classification problem through deep learning,” Pattern Recognit. 61, 405–416 (2017).
[Crossref]

Laligant, O.

Land, E. H.

E. H. Land, “The retinex theory of color vision,” Sci. Am. 237(6), 108–128 (1977).
[Crossref] [PubMed]

Lee, H. C.

Li, B.

B. Li, W. Xiong, W. Hu, B. Funt, and J. Xing, “Multi-cue illumination estimation via a tree-structured group joint sparse representation,” Int. J. Comput. Vis. 117(1), 21–47 (2016).
[Crossref]

B. Li, D. Xu, W. Xiong, and S. Feng, “Color constancy using achromatic surface,” Color Res. Appl. 35(4), 304–332 (2010).
[Crossref]

Li, C.

K. Yang, S. Gao, C. Li, and Y. Li, “Efficient color boundary detection with color-opponent mechanisms,” in IEEE Conference on Computer Vision and Pattern Recognition (2013), pp. 2810–2817.

S. Gao, K. Yang, C. Li, and Y. Li, “A color constancy model with double-opponency mechanisms,” in IEEE International Conference on Computer Vision (2013), pp. 929–936.

S. Gao, W. Han, K. Yang, C. Li, and Y. Li, “Efficient color constancy with local surface reflectance statistics,” in European Conference on Computer Vision (2014), pp. 158–173.

Li, C. Y.

Li, M. M.

C. Tang, X. W. Liu, M. M. Li, P. C. Wang, J. J. Chen, L. Z. Wang, and W. Q. Li, “Robust Unsupervised Feature Selection via Dual Self-representation and Manifold Regularization,” Knowl-based. Syst. 145, 109–120 (2018).

Li, W. Q.

C. Tang, X. W. Liu, M. M. Li, P. C. Wang, J. J. Chen, L. Z. Wang, and W. Q. Li, “Robust Unsupervised Feature Selection via Dual Self-representation and Manifold Regularization,” Knowl-based. Syst. 145, 109–120 (2018).

C. Tang, P. C. Wang, C. Q. Zhang, and W. Q. Li, “Salient Object Detection via Weighted Low Rank Matrix Recovery,” IEEE Signal Process. Lett. 24(4), 490–494 (2017).
[Crossref]

Li, Y.

S. Gao, W. Han, K. Yang, C. Li, and Y. Li, “Efficient color constancy with local surface reflectance statistics,” in European Conference on Computer Vision (2014), pp. 158–173.

Y. Li, R. T. Tan, and M. S. Brown, “Nighttime haze removal with glow and multiple light colors,” in IEEE International Conference on Computer Vision (2015), pp. 226–234.

S. Gao, K. Yang, C. Li, and Y. Li, “A color constancy model with double-opponency mechanisms,” in IEEE International Conference on Computer Vision (2013), pp. 929–936.

K. Yang, S. Gao, C. Li, and Y. Li, “Efficient color boundary detection with color-opponent mechanisms,” in IEEE Conference on Computer Vision and Pattern Recognition (2013), pp. 2810–2817.

Li, Y. J.

S. B. Gao, M. Zhang, C. Y. Li, and Y. J. Li, “Improving color constancy by discounting the variation of camera spectral sensitivity,” J. Opt. Soc. Am. A 34(8), 1448–1462 (2017).
[Crossref] [PubMed]

K. F. Yang, S. B. Gao, and Y. J. Li, “Efficient illuminant estimation for color constancy using grey pixels,” in IEEE Conference on Computer Vision and Pattern Recognition (2015), pp. 2254–2263.

Liu, W. C.

W. T. Chen, W. C. Liu, and M. S. Chen, “Adaptive color feature extraction based on image color distributions,” IEEE Trans. Image Process. 19(8), 2005–2016 (2010).
[Crossref] [PubMed]

Liu, X. W.

C. Tang, X. W. Liu, M. M. Li, P. C. Wang, J. J. Chen, L. Z. Wang, and W. Q. Li, “Robust Unsupervised Feature Selection via Dual Self-representation and Manifold Regularization,” Knowl-based. Syst. 145, 109–120 (2018).

Loy, C. C.

W. Shi, C. C. Loy, and X. Tang, “Deep specialized network for illuminant estimation,” in European Conference on Computer Vision (2016), pp. 371–387.

Luo, M. R.

M. R. Luo, G. Cui, and M. Georgoula, “Colour difference evaluation for white light sources,” Light. Res. Technol. 47(3), 360–369 (2015).
[Crossref]

Marques, J. S.

C. Barata, M. E. Celebi, and J. S. Marques, “Improving dermoscopy image classification using color constancy,” IEEE J. Biomed. Health Inform. 19(3), 1146–1152 (2015).
[PubMed]

Matas, J.

Y. Qian, K. Chen, J. Nikkanen, J. K. Kamarainen, and J. Matas, “Recurrent Color Constancy,” in IEEE International Conference on Computer Vision (2017), pp. 5459–5467.

Minka, T.

P. V. Gehler, C. Rother, A. Blake, T. Minka, and T. Sharp, “Bayesian color constancy revisited,” in IEEE Conference on Computer Vision and Pattern Recognition (2008), pp. 1–8.

Mosny, M.

B. Funt and M. Mosny, “Removing outliers in illumination estimation,” in Color and Imaging Conference (2012), pp. 105–110.

Nikkanen, J.

Y. Qian, K. Chen, J. Nikkanen, J. K. Kamarainen, and J. Matas, “Recurrent Color Constancy,” in IEEE International Conference on Computer Vision (2017), pp. 5459–5467.

Nishino, K.

Oh, S. W.

S. W. Oh and S. J. Kim, “Approaching the computational color constancy as a classification problem through deep learning,” Pattern Recognit. 61, 405–416 (2017).
[Crossref]

Ohno, Y.

Y. Ohno and P. Blattner, “Chromaticity difference specification for light sources,” International Commission on Illumination, Tech. Rep. CIE TN 001, 2014 (2014).

Prasad, D. K.

Price, B.

D. Cheng, B. Price, S. Cohen, and M. S. Brown, “Effective learning-based illuminant estimation using simple features,” in IEEE Conference on Computer Vision and Pattern Recognition (2015), pp. 1000–1008.

Qian, Y.

Y. Qian, K. Chen, J. Nikkanen, J. K. Kamarainen, and J. Matas, “Recurrent Color Constancy,” in IEEE International Conference on Computer Vision (2017), pp. 5459–5467.

Rey, P. A. T.

H. R. V. Joze, M. S. Drew, G. D. Finlayson, and P. A. T. Rey, “The role of bright pixels in illumination estimation,” in Color and Imaging Conference (2012), pp. 41–46.

Rother, C.

P. V. Gehler, C. Rother, A. Blake, T. Minka, and T. Sharp, “Bayesian color constancy revisited,” in IEEE Conference on Computer Vision and Pattern Recognition (2008), pp. 1–8.

Schettini, R.

S. Bianco, C. Cusano, and R. Schettini, “Single and multiple illuminant estimation using convolutional neural networks,” IEEE Trans. Image Process. 26(9), 4347–4362 (2017).
[Crossref] [PubMed]

S. Bianco, G. Ciocca, C. Cusano, and R. Schettini, “Automatic color constancy algorithm selection and combination,” Pattern Recognit. 43(3), 695–705 (2010).
[Crossref]

S. Bianco, G. Ciocca, C. Cusano, and R. Schettini, “Improving color constancy using indoor-outdoor image classification,” IEEE Trans. Image Process. 17(12), 2381–2392 (2008).
[Crossref] [PubMed]

S. Bianco, C. Cusano, and R. Schettini, “Color constancy using CNNs,” in IEEE Conference on Computer Vision and Pattern Recognition (2015), pp. 81–89.

Sharp, T.

P. V. Gehler, C. Rother, A. Blake, T. Minka, and T. Sharp, “Bayesian color constancy revisited,” in IEEE Conference on Computer Vision and Pattern Recognition (2008), pp. 1–8.

Shi, L.

L. Shi, W. Xiong, and B. Funt, “Illumination estimation via thin-plate spline interpolation,” J. Opt. Soc. Am. A 28(5), 940–948 (2011).
[Crossref] [PubMed]

W. Xiong, B. Funt, and L. Shi, “Automatic white balancing via grey surface identification,” in Proceeding of 15th Color Imaging Conference: Color Science, Systems and Applications.Springfield (2007), pp. 5–9.

Shi, W.

W. Shi, C. C. Loy, and X. Tang, “Deep specialized network for illuminant estimation,” in European Conference on Computer Vision (2016), pp. 371–387.

Suo, J.

Tan, R. T.

R. T. Tan, K. Nishino, and K. Ikeuchi, “Color constancy through inverse-intensity chromaticity space,” J. Opt. Soc. Am. A 21(3), 321–334 (2004).
[Crossref] [PubMed]

Y. Li, R. T. Tan, and M. S. Brown, “Nighttime haze removal with glow and multiple light colors,” in IEEE International Conference on Computer Vision (2015), pp. 226–234.

Tang, C.

C. Tang, X. W. Liu, M. M. Li, P. C. Wang, J. J. Chen, L. Z. Wang, and W. Q. Li, “Robust Unsupervised Feature Selection via Dual Self-representation and Manifold Regularization,” Knowl-based. Syst. 145, 109–120 (2018).

C. Tang, P. C. Wang, C. Q. Zhang, and W. Q. Li, “Salient Object Detection via Weighted Low Rank Matrix Recovery,” IEEE Signal Process. Lett. 24(4), 490–494 (2017).
[Crossref]

Tang, X.

W. Shi, C. C. Loy, and X. Tang, “Deep specialized network for illuminant estimation,” in European Conference on Computer Vision (2016), pp. 371–387.

Thomas, J. B.

Trezzi, E.

G. D. Finlayson and E. Trezzi, “Shades of gray and colour constancy,” in Color and Imaging Conference (2004), pp. 37–41.

Tsai, Y. T.

J. T. Barron and Y. T. Tsai, “Fast fourier color constancy,” in IEEE Conference on Computer Vision and Pattern Recognition (2017), pp. 21–26.

van de Weijer, J.

A. Gijsenij, T. Gevers, and J. van de Weijer, “Improving color constancy by photometric edge weighting,” IEEE Trans. Pattern Anal. Mach. Intell. 34(5), 918–929 (2012).
[Crossref] [PubMed]

A. Gijsenij, T. Gevers, and J. van de Weijer, “Computational color constancy: Survey and experiments,” IEEE Trans. Image Process. 20(9), 2475–2489 (2011).
[Crossref] [PubMed]

A. Gijsenij, T. Gevers, and J. van De Weijer, “Generalized gamut mapping using image derivative structures for color constancy,” Int. J. Comput. Vis. 86(2–3), 127–139 (2010).
[Crossref]

J. van de Weijer, T. Gevers, and A. Gijsenij, “Edge-based color constancy,” IEEE Trans. Image Process. 16(9), 2207–2214 (2007).
[Crossref] [PubMed]

M. Danelljan, F. S. Khan, M. Felsberg, and J. van de Weijer, “Adaptive color attributes for real-time visual tracking,” in IEEE Conference on Computer Vision and Pattern Recognition (2014), pp. 1090–1097.

Wandell, B. A.

Wang, H.

Wang, L. Z.

C. Tang, X. W. Liu, M. M. Li, P. C. Wang, J. J. Chen, L. Z. Wang, and W. Q. Li, “Robust Unsupervised Feature Selection via Dual Self-representation and Manifold Regularization,” Knowl-based. Syst. 145, 109–120 (2018).

Wang, P. C.

C. Tang, X. W. Liu, M. M. Li, P. C. Wang, J. J. Chen, L. Z. Wang, and W. Q. Li, “Robust Unsupervised Feature Selection via Dual Self-representation and Manifold Regularization,” Knowl-based. Syst. 145, 109–120 (2018).

C. Tang, P. C. Wang, C. Q. Zhang, and W. Q. Li, “Salient Object Detection via Weighted Low Rank Matrix Recovery,” IEEE Signal Process. Lett. 24(4), 490–494 (2017).
[Crossref]

Weihua Xiong,

Bing Li, Weihua Xiong, Weiming Hu, and B. Funt, “Evaluating combinational illumination estimation methods on real-world images,” IEEE Trans. Image Process. 23(3), 1194–1209 (2014).
[Crossref] [PubMed]

Weiming Hu,

Bing Li, Weihua Xiong, Weiming Hu, and B. Funt, “Evaluating combinational illumination estimation methods on real-world images,” IEEE Trans. Image Process. 23(3), 1194–1209 (2014).
[Crossref] [PubMed]

Xing, J.

B. Li, W. Xiong, W. Hu, B. Funt, and J. Xing, “Multi-cue illumination estimation via a tree-structured group joint sparse representation,” Int. J. Comput. Vis. 117(1), 21–47 (2016).
[Crossref]

Xiong, W.

B. Li, W. Xiong, W. Hu, B. Funt, and J. Xing, “Multi-cue illumination estimation via a tree-structured group joint sparse representation,” Int. J. Comput. Vis. 117(1), 21–47 (2016).
[Crossref]

L. Shi, W. Xiong, and B. Funt, “Illumination estimation via thin-plate spline interpolation,” J. Opt. Soc. Am. A 28(5), 940–948 (2011).
[Crossref] [PubMed]

B. Li, D. Xu, W. Xiong, and S. Feng, “Color constancy using achromatic surface,” Color Res. Appl. 35(4), 304–332 (2010).
[Crossref]

W. Xiong, B. Funt, and L. Shi, “Automatic white balancing via grey surface identification,” in Proceeding of 15th Color Imaging Conference: Color Science, Systems and Applications.Springfield (2007), pp. 5–9.

Xu, D.

B. Li, D. Xu, W. Xiong, and S. Feng, “Color constancy using achromatic surface,” Color Res. Appl. 35(4), 304–332 (2010).
[Crossref]

Yang, K.

K. Yang, S. Gao, C. Li, and Y. Li, “Efficient color boundary detection with color-opponent mechanisms,” in IEEE Conference on Computer Vision and Pattern Recognition (2013), pp. 2810–2817.

S. Gao, K. Yang, C. Li, and Y. Li, “A color constancy model with double-opponency mechanisms,” in IEEE International Conference on Computer Vision (2013), pp. 929–936.

S. Gao, W. Han, K. Yang, C. Li, and Y. Li, “Efficient color constancy with local surface reflectance statistics,” in European Conference on Computer Vision (2014), pp. 158–173.

Yang, K. F.

K. F. Yang, S. B. Gao, and Y. J. Li, “Efficient illuminant estimation for color constancy using grey pixels,” in IEEE Conference on Computer Vision and Pattern Recognition (2015), pp. 2254–2263.

Zhang, C. Q.

C. Tang, P. C. Wang, C. Q. Zhang, and W. Q. Li, “Salient Object Detection via Weighted Low Rank Matrix Recovery,” IEEE Signal Process. Lett. 24(4), 490–494 (2017).
[Crossref]

Zhang, M.

Zickler, T.

A. Chakrabarti, K. Hirakawa, and T. Zickler, “Color constancy with spatio-spectral statistics,” IEEE Trans. Pattern Anal. Mach. Intell. 34(8), 1509–1519 (2012).
[Crossref] [PubMed]

Color Res. Appl. (2)

S. D. Hordley, “Scene illuminant estimation: past, present, and future,” Color Res. Appl. 31(4), 303–314 (2006).
[Crossref]

B. Li, D. Xu, W. Xiong, and S. Feng, “Color constancy using achromatic surface,” Color Res. Appl. 35(4), 304–332 (2010).
[Crossref]

IEEE J. Biomed. Health Inform. (1)

C. Barata, M. E. Celebi, and J. S. Marques, “Improving dermoscopy image classification using color constancy,” IEEE J. Biomed. Health Inform. 19(3), 1146–1152 (2015).
[PubMed]

IEEE Signal Process. Lett. (1)

C. Tang, P. C. Wang, C. Q. Zhang, and W. Q. Li, “Salient Object Detection via Weighted Low Rank Matrix Recovery,” IEEE Signal Process. Lett. 24(4), 490–494 (2017).
[Crossref]

IEEE Trans. Image Process. (7)

S. Bianco, G. Ciocca, C. Cusano, and R. Schettini, “Improving color constancy using indoor-outdoor image classification,” IEEE Trans. Image Process. 17(12), 2381–2392 (2008).
[Crossref] [PubMed]

W. T. Chen, W. C. Liu, and M. S. Chen, “Adaptive color feature extraction based on image color distributions,” IEEE Trans. Image Process. 19(8), 2005–2016 (2010).
[Crossref] [PubMed]

A. Gijsenij, T. Gevers, and J. van de Weijer, “Computational color constancy: Survey and experiments,” IEEE Trans. Image Process. 20(9), 2475–2489 (2011).
[Crossref] [PubMed]

J. van de Weijer, T. Gevers, and A. Gijsenij, “Edge-based color constancy,” IEEE Trans. Image Process. 16(9), 2207–2214 (2007).
[Crossref] [PubMed]

K. Barnard, V. Cardei, and B. Funt, “A comparison of computational color constancy algorithms--part I: methodology and experiments with synthesized data,” IEEE Trans. Image Process. 11(9), 972–983 (2002).
[Crossref] [PubMed]

Bing Li, Weihua Xiong, Weiming Hu, and B. Funt, “Evaluating combinational illumination estimation methods on real-world images,” IEEE Trans. Image Process. 23(3), 1194–1209 (2014).
[Crossref] [PubMed]

S. Bianco, C. Cusano, and R. Schettini, “Single and multiple illuminant estimation using convolutional neural networks,” IEEE Trans. Image Process. 26(9), 4347–4362 (2017).
[Crossref] [PubMed]

IEEE Trans. Pattern Anal. Mach. Intell. (4)

A. Chakrabarti, K. Hirakawa, and T. Zickler, “Color constancy with spatio-spectral statistics,” IEEE Trans. Pattern Anal. Mach. Intell. 34(8), 1509–1519 (2012).
[Crossref] [PubMed]

A. Gijsenij and T. Gevers, “Color constancy using natural image statistics and scene semantics,” IEEE Trans. Pattern Anal. Mach. Intell. 33(4), 687–698 (2011).
[Crossref] [PubMed]

H. R. V. Joze and M. S. Drew, “Exemplar-Based Color Constancy and Multiple Illumination,” IEEE Trans. Pattern Anal. Mach. Intell. 36(5), 860–873 (2014).
[Crossref] [PubMed]

A. Gijsenij, T. Gevers, and J. van de Weijer, “Improving color constancy by photometric edge weighting,” IEEE Trans. Pattern Anal. Mach. Intell. 34(5), 918–929 (2012).
[Crossref] [PubMed]

Int. J. Comput. Vis. (2)

A. Gijsenij, T. Gevers, and J. van De Weijer, “Generalized gamut mapping using image derivative structures for color constancy,” Int. J. Comput. Vis. 86(2–3), 127–139 (2010).
[Crossref]

B. Li, W. Xiong, W. Hu, B. Funt, and J. Xing, “Multi-cue illumination estimation via a tree-structured group joint sparse representation,” Int. J. Comput. Vis. 117(1), 21–47 (2016).
[Crossref]

International Commission on Illumination, Tech. Rep. CIE TN (1)

Y. Ohno and P. Blattner, “Chromaticity difference specification for light sources,” International Commission on Illumination, Tech. Rep. CIE TN 001, 2014 (2014).

J. Franklin Inst. (1)

G. Buchsbaum, “A spatial processor model for object colour perception,” J. Franklin Inst. 310(1), 1–261 (1980).
[Crossref]

J. Opt. Soc. Am. A (7)

Knowl-based. Syst. (1)

C. Tang, X. W. Liu, M. M. Li, P. C. Wang, J. J. Chen, L. Z. Wang, and W. Q. Li, “Robust Unsupervised Feature Selection via Dual Self-representation and Manifold Regularization,” Knowl-based. Syst. 145, 109–120 (2018).

Light. Res. Technol. (1)

M. R. Luo, G. Cui, and M. Georgoula, “Colour difference evaluation for white light sources,” Light. Res. Technol. 47(3), 360–369 (2015).
[Crossref]

Opt. Express (1)

Pattern Recognit. (2)

S. W. Oh and S. J. Kim, “Approaching the computational color constancy as a classification problem through deep learning,” Pattern Recognit. 61, 405–416 (2017).
[Crossref]

S. Bianco, G. Ciocca, C. Cusano, and R. Schettini, “Automatic color constancy algorithm selection and combination,” Pattern Recognit. 43(3), 695–705 (2010).
[Crossref]

Sci. Am. (1)

E. H. Land, “The retinex theory of color vision,” Sci. Am. 237(6), 108–128 (1977).
[Crossref] [PubMed]

Other (22)

G. D. Finlayson and E. Trezzi, “Shades of gray and colour constancy,” in Color and Imaging Conference (2004), pp. 37–41.

Y. Li, R. T. Tan, and M. S. Brown, “Nighttime haze removal with glow and multiple light colors,” in IEEE International Conference on Computer Vision (2015), pp. 226–234.

W. Shi, C. C. Loy, and X. Tang, “Deep specialized network for illuminant estimation,” in European Conference on Computer Vision (2016), pp. 371–387.

M. Danelljan, F. S. Khan, M. Felsberg, and J. van de Weijer, “Adaptive color attributes for real-time visual tracking,” in IEEE Conference on Computer Vision and Pattern Recognition (2014), pp. 1090–1097.

Y. Qian, K. Chen, J. Nikkanen, J. K. Kamarainen, and J. Matas, “Recurrent Color Constancy,” in IEEE International Conference on Computer Vision (2017), pp. 5459–5467.

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Figures (6)

Fig. 1
Fig. 1 Schematic diagram of the Yang’s method for illumination estimation. The red pixels in the second rightmost image indicate the location of the detected grey pixels, and the rightmost image represents the corrected image with the estimated illuminant.
Fig. 2
Fig. 2 Relationship between the angular error and the parameter n% in the different data sets.
Fig. 3
Fig. 3 Relationship between the angular error and the parameter η of the proposed method in the different data sets.
Fig. 4
Fig. 4 The probability density distribution of the angular error in the Gehler-Shi data set.
Fig. 5
Fig. 5 (a) Results of an image consisting of several chromatic color patches. The improved method can detect more grey pixels (yellow rectangle) on the boundaries of the color patches. (b) Results of an image containing grey patches. The angular error of the improved method is smaller.
Fig. 6
Fig. 6 Corrected images using the estimated illuminant from four different methods including the improved one. The angular error is indicated at the lower right corner of the image.

Tables (4)

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Table 1 Angular error of the improved method and other methods on the Gehler-Shi data set

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Table 2 Angular error of the improved method and other methods on the SFU Grey Ball data set

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Table 3 Angular error of the improved method and other methods on the SFU laboratory data set

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Table 4 Mean chromaticity difference Δu'v' of the improved method and other methods on three data sets

Equations (10)

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I c ( x , y ) = ω E ( x , y , λ ) R ( x , y , λ ) S c ( λ ) d λ ,
I c ( x , y ) = E c ( x , y ) × R c ( x , y ) ,
I c log ( x , y ) = log ( E c ( x , y ) × R c ( x , y ) ) = log ( E c ( x , y ) ) + log ( R c ( x , y ) ) . = E c log ( x , y ) + R c log ( x , y )
C c ( x , y ) = 1 N i = 1 N ( I c log ( x , y ) I c , i log ( x m , y m ) ) 2 ,
C r ( x , y ) = C g ( x , y ) = C b ( x , y ) 0.
D I ( x , y ) = 1- 1 3 c { r , g , b } | C c ( x , y ) - C ¯ ( x , y ) C ¯ ( x , y ) | ,
D I ( x , y ) = A F η { D I ( x , y ) × I ¯ ( x , y ) } ,
e c = 1 N G ( x , y ) G T n I c ( x , y ) , c { r , g , b } ,
e r r a n g u l a r ( e e s t , e g t ) = cos 1 ( e e s t e g t e e s t e g t ) ,
Δ u ' v ' = ( u 2 ' u 1 ' ) 2 + ( v 2 ' v 1 ' ) 2

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