On Finding Gray Pixels

We propose a novel grayness index for finding gray pixels and demonstrate its effectiveness and efficiency in illumination estimation. The grayness index, GI in short, is derived using the Dichromatic Reflection Model and is learning-free. GI allows to estimate one or multiple illumination sources in color-biased images. On standard single-illumination and multiple-illumination estimation benchmarks, GI outperforms state-of-the-art statistical methods and many recent deep methods. GI is simple and fast, written in a few dozen lines of code, processing a 1080p image in ~0.4 seconds with a non-optimized Matlab code.

[1]  Graham D. Finlayson,et al.  Corrected-Moment Illuminant Estimation , 2013, 2013 IEEE International Conference on Computer Vision.

[2]  Jiri Matas,et al.  Recurrent Color Constancy , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[3]  Theo Gevers,et al.  Color Constancy Using Natural Image Statistics and Scene Semantics , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Xiaoou Tang,et al.  Deep Specialized Network for Illuminant Estimation , 2016, ECCV.

[5]  Graham D. Finlayson,et al.  Shades of Gray and Colour Constancy , 2004, CIC.

[6]  Keigo Hirakawa,et al.  Color Constancy with Spatio-Spectral Statistics , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Hsien-Che Lee,et al.  Modeling Light Reflection for Computer Color Vision , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  Ayan Chakrabarti,et al.  Color Constancy by Learning to Predict Chromaticity from Luminance , 2015, NIPS.

[9]  Andrew Blake,et al.  Bayesian color constancy revisited , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[10]  Yun-Ta Tsai,et al.  Fast Fourier Color Constancy , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[11]  Stephen Lin,et al.  FC^4: Fully Convolutional Color Constancy with Confidence-Weighted Pooling , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[12]  Michael S. Brown,et al.  Two Illuminant Estimation and User Correction Preference , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[13]  Gerald Schaefer,et al.  Convex and non-convex illuminant constraints for dichromatic colour constancy , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[14]  Xinlei Chen,et al.  Mind's eye: A recurrent visual representation for image caption generation , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[15]  Gerald Schaefer,et al.  Solving for Colour Constancy using a Constrained Dichromatic Reflection Model , 2001, International Journal of Computer Vision.

[16]  Raimondo Schettini,et al.  Computational color constancy , 2011, 3rd European Workshop on Visual Information Processing.

[17]  Kai-Fu Yang,et al.  Color Constancy Using Double-Opponency , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[18]  Jiri Matas,et al.  Revisiting Gray Pixel for Statistical Illumination Estimation , 2019, VISIGRAPP.

[19]  Marc Ebner,et al.  Color Constancy , 2007, Computer Vision, A Reference Guide.

[20]  G. Buchsbaum.  A spatial processor model for object colour perception , 1980 .

[21]  Byoung-Ho Kang,et al.  Automatic White Balancing via Gray Surface Identification , 2007, CIC.

[22]  Brian V. Funt,et al.  A comparison of computational color constancy algorithms. I: Methodology and experiments with synthesized data , 2002, IEEE Trans. Image Process..

[23]  Joost van de Weijer,et al.  Generalized Gamut Mapping using Image Derivative Structures for Color Constancy , 2008, International Journal of Computer Vision.

[24]  Steven A. Shafer,et al.  Using color to separate reflection components , 1985 .

[25]  Sung-Min Woo,et al.  Improving Color Constancy in an Ambient Light Environment Using the Phong Reflection Model , 2018, IEEE Transactions on Image Processing.

[26]  Yongjie Li,et al.  Efficient Color Constancy with Local Surface Reflectance Statistics , 2014, ECCV.

[27]  Brian V. Funt,et al.  A comparison of computational color constancy Algorithms. II. Experiments with image data , 2002, IEEE Trans. Image Process..

[28]  Joost van de Weijer,et al.  Author Manuscript, Published in "ieee Transactions on Image Processing Edge-based Color Constancy , 2022 .

[29]  D. Foster.  Color constancy , 2011, Vision Research.

[30]  Joost van de Weijer,et al.  Computational Color Constancy: Survey and Experiments , 2011, IEEE Transactions on Image Processing.

[31]  Joost van de Weijer,et al.  Improving Color Constancy by Photometric Edge Weighting , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[32]  Ming Zhang,et al.  Improving Color Constancy by Discounting the Variation of Camera Spectral Sensitivity , 2016, Journal of the Optical Society of America. A, Optics, image science, and vision.

[33]  Jonathan T. Barron,et al.  Convolutional Color Constancy , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[34]  D H Brainard,et al.  Analysis of the retinex theory of color vision. , 1986, Journal of the Optical Society of America. A, Optics and image science.

[35]  Michael S. Brown,et al.  Effective learning-based illuminant estimation using simple features , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[36]  Jiri Matas,et al.  Deep structured-output regression learning for computational color constancy , 2016, 2016 23rd International Conference on Pattern Recognition (ICPR).

[37]  Shoji Tominaga,et al.  MULTICHANNEL VISION SYSTEM FOR ESTIMATING SURFACE AND ILLUMINATION FUNCTIONS , 1996 .

[38]  Joost van de Weijer,et al.  Multi-Illuminant Estimation With Conditional Random Fields , 2014, IEEE Transactions on Image Processing.

[39]  Mark S. Drew,et al.  Exemplar-Based Color Constancy and Multiple Illumination , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[40]  Robby T. Tan,et al.  Color constancy through inverse-intensity chromaticity space. , 2004 .

[41]  K. Ikeuchi,et al.  Color constancy through inverse-intensity chromaticity space. , 2004, Journal of the Optical Society of America. A, Optics, image science, and vision.

[42]  Kai-Fu Yang,et al.  Efficient illuminant estimation for color constancy using grey pixels , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[43]  GijsenijA.,et al.  Computational Color Constancy , 2011 .

[44]  C. Alejandro Parraga,et al.  Colour Constancy Beyond the Classical Receptive Field , 2018, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[45]  Dilip K Prasad,et al.  Illuminant estimation for color constancy: why spatial-domain methods work and the role of the color distribution. , 2014, Journal of the Optical Society of America. A, Optics, image science, and vision.