Color Correction Method for Digital Camera Based on Variable-Exponent Polynomial Regression

Yingjie Zhou; Kun Gao; Yue Guo; Zeyang Dou; Haobo Cheng; Zhuoyi Chen

doi:10.1007/978-981-13-6504-1_15

Color Correction Method for Digital Camera Based on Variable-Exponent Polynomial Regression

Yingjie Zhou, Kun Gao^*, Yue Guo, Zeyang Dou, Haobo Cheng, Zhuoyi Chen

^*Corresponding author for this work

School of Optics and Photonics

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Citations (Scopus)

Abstract

Subject to the response uniformity of photoelectric sensors, the captured raw images always have serious chroma distortions. How to determine the mapping matrix between RGB and XYZ color spaces is important for the color distortion correction. However, the commonly used algorithms cannot give consideration to the precision and the adaptability. A more reasonable mapping algorithm based on variable-exponent polynomial regression is proposed to evaluate the mapping matrix coefficients. Variable-exponent regularization with the L_ρ-norm (1 < ρ < 2) combines the features of lasso regression and ridge regression methods, owning both the sparsity and smoothing properties. The optimal solution for the variable-exponent regularization is given using lagged fix-point iteration method. Data from the standard color correction experiments are used to test the variable-exponent, lasso, ridge, and least-squares regression algorithms with different polynomial regression models. The results demonstrate that the proposed algorithm has the best performance.

Original language	English
Title of host publication	Communications, Signal Processing, and Systems - Proceedings of the 2018 CSPS Volume II
Subtitle of host publication	Signal Processing
Editors	Qilian Liang, Xin Liu, Zhenyu Na, Wei Wang, Jiasong Mu, Baoju Zhang
Publisher	Springer Verlag
Pages	111-118
Number of pages	8
ISBN (Print)	9789811365034
DOIs	https://doi.org/10.1007/978-981-13-6504-1_15
Publication status	Published - 2020
Event	International Conference on Communications, Signal Processing, and Systems, CSPS 2018 - Dalian, China Duration: 14 Jul 2018 → 16 Jul 2018

Publication series

Name	Lecture Notes in Electrical Engineering
Volume	516
ISSN (Print)	1876-1100
ISSN (Electronic)	1876-1119

Conference

Conference	International Conference on Communications, Signal Processing, and Systems, CSPS 2018
Country/Territory	China
City	Dalian
Period	14/07/18 → 16/07/18

Keywords

Color correction
Polynomial regression
Regularization
Variable-exponent

Access to Document

10.1007/978-981-13-6504-1_15

Cite this

Zhou, Y., Gao, K., Guo, Y., Dou, Z., Cheng, H., & Chen, Z. (2020). Color Correction Method for Digital Camera Based on Variable-Exponent Polynomial Regression. In Q. Liang, X. Liu, Z. Na, W. Wang, J. Mu, & B. Zhang (Eds.), Communications, Signal Processing, and Systems - Proceedings of the 2018 CSPS Volume II: Signal Processing (pp. 111-118). (Lecture Notes in Electrical Engineering; Vol. 516). Springer Verlag. https://doi.org/10.1007/978-981-13-6504-1_15

Zhou, Yingjie ; Gao, Kun ; Guo, Yue et al. / Color Correction Method for Digital Camera Based on Variable-Exponent Polynomial Regression. Communications, Signal Processing, and Systems - Proceedings of the 2018 CSPS Volume II: Signal Processing. editor / Qilian Liang ; Xin Liu ; Zhenyu Na ; Wei Wang ; Jiasong Mu ; Baoju Zhang. Springer Verlag, 2020. pp. 111-118 (Lecture Notes in Electrical Engineering).

@inproceedings{1b3dfd65a1384baca11e375398da858c,

title = "Color Correction Method for Digital Camera Based on Variable-Exponent Polynomial Regression",

abstract = "Subject to the response uniformity of photoelectric sensors, the captured raw images always have serious chroma distortions. How to determine the mapping matrix between RGB and XYZ color spaces is important for the color distortion correction. However, the commonly used algorithms cannot give consideration to the precision and the adaptability. A more reasonable mapping algorithm based on variable-exponent polynomial regression is proposed to evaluate the mapping matrix coefficients. Variable-exponent regularization with the Lρ-norm (1 < ρ < 2) combines the features of lasso regression and ridge regression methods, owning both the sparsity and smoothing properties. The optimal solution for the variable-exponent regularization is given using lagged fix-point iteration method. Data from the standard color correction experiments are used to test the variable-exponent, lasso, ridge, and least-squares regression algorithms with different polynomial regression models. The results demonstrate that the proposed algorithm has the best performance.",

keywords = "Color correction, Polynomial regression, Regularization, Variable-exponent",

author = "Yingjie Zhou and Kun Gao and Yue Guo and Zeyang Dou and Haobo Cheng and Zhuoyi Chen",

note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Singapore Pte Ltd.; International Conference on Communications, Signal Processing, and Systems, CSPS 2018 ; Conference date: 14-07-2018 Through 16-07-2018",

year = "2020",

doi = "10.1007/978-981-13-6504-1_15",

language = "English",

isbn = "9789811365034",

series = "Lecture Notes in Electrical Engineering",

publisher = "Springer Verlag",

pages = "111--118",

editor = "Qilian Liang and Xin Liu and Zhenyu Na and Wei Wang and Jiasong Mu and Baoju Zhang",

booktitle = "Communications, Signal Processing, and Systems - Proceedings of the 2018 CSPS Volume II",

address = "Germany",

}

Zhou, Y, Gao, K, Guo, Y, Dou, Z, Cheng, H & Chen, Z 2020, Color Correction Method for Digital Camera Based on Variable-Exponent Polynomial Regression. in Q Liang, X Liu, Z Na, W Wang, J Mu & B Zhang (eds), Communications, Signal Processing, and Systems - Proceedings of the 2018 CSPS Volume II: Signal Processing. Lecture Notes in Electrical Engineering, vol. 516, Springer Verlag, pp. 111-118, International Conference on Communications, Signal Processing, and Systems, CSPS 2018, Dalian, China, 14/07/18. https://doi.org/10.1007/978-981-13-6504-1_15

Color Correction Method for Digital Camera Based on Variable-Exponent Polynomial Regression. / Zhou, Yingjie; Gao, Kun; Guo, Yue et al.
Communications, Signal Processing, and Systems - Proceedings of the 2018 CSPS Volume II: Signal Processing. ed. / Qilian Liang; Xin Liu; Zhenyu Na; Wei Wang; Jiasong Mu; Baoju Zhang. Springer Verlag, 2020. p. 111-118 (Lecture Notes in Electrical Engineering; Vol. 516).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Color Correction Method for Digital Camera Based on Variable-Exponent Polynomial Regression

AU - Zhou, Yingjie

AU - Gao, Kun

AU - Guo, Yue

AU - Dou, Zeyang

AU - Cheng, Haobo

AU - Chen, Zhuoyi

PY - 2020

Y1 - 2020

N2 - Subject to the response uniformity of photoelectric sensors, the captured raw images always have serious chroma distortions. How to determine the mapping matrix between RGB and XYZ color spaces is important for the color distortion correction. However, the commonly used algorithms cannot give consideration to the precision and the adaptability. A more reasonable mapping algorithm based on variable-exponent polynomial regression is proposed to evaluate the mapping matrix coefficients. Variable-exponent regularization with the Lρ-norm (1 < ρ < 2) combines the features of lasso regression and ridge regression methods, owning both the sparsity and smoothing properties. The optimal solution for the variable-exponent regularization is given using lagged fix-point iteration method. Data from the standard color correction experiments are used to test the variable-exponent, lasso, ridge, and least-squares regression algorithms with different polynomial regression models. The results demonstrate that the proposed algorithm has the best performance.

AB - Subject to the response uniformity of photoelectric sensors, the captured raw images always have serious chroma distortions. How to determine the mapping matrix between RGB and XYZ color spaces is important for the color distortion correction. However, the commonly used algorithms cannot give consideration to the precision and the adaptability. A more reasonable mapping algorithm based on variable-exponent polynomial regression is proposed to evaluate the mapping matrix coefficients. Variable-exponent regularization with the Lρ-norm (1 < ρ < 2) combines the features of lasso regression and ridge regression methods, owning both the sparsity and smoothing properties. The optimal solution for the variable-exponent regularization is given using lagged fix-point iteration method. Data from the standard color correction experiments are used to test the variable-exponent, lasso, ridge, and least-squares regression algorithms with different polynomial regression models. The results demonstrate that the proposed algorithm has the best performance.

KW - Color correction

KW - Polynomial regression

KW - Regularization

KW - Variable-exponent

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U2 - 10.1007/978-981-13-6504-1_15

DO - 10.1007/978-981-13-6504-1_15

M3 - Conference contribution

AN - SCOPUS:85071420827

SN - 9789811365034

T3 - Lecture Notes in Electrical Engineering

SP - 111

EP - 118

BT - Communications, Signal Processing, and Systems - Proceedings of the 2018 CSPS Volume II

A2 - Liang, Qilian

A2 - Liu, Xin

A2 - Na, Zhenyu

A2 - Wang, Wei

A2 - Mu, Jiasong

A2 - Zhang, Baoju

PB - Springer Verlag

T2 - International Conference on Communications, Signal Processing, and Systems, CSPS 2018

Y2 - 14 July 2018 through 16 July 2018

ER -

Zhou Y, Gao K, Guo Y, Dou Z, Cheng H, Chen Z. Color Correction Method for Digital Camera Based on Variable-Exponent Polynomial Regression. In Liang Q, Liu X, Na Z, Wang W, Mu J, Zhang B, editors, Communications, Signal Processing, and Systems - Proceedings of the 2018 CSPS Volume II: Signal Processing. Springer Verlag. 2020. p. 111-118. (Lecture Notes in Electrical Engineering). doi: 10.1007/978-981-13-6504-1_15

Color Correction Method for Digital Camera Based on Variable-Exponent Polynomial Regression

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