A Color Correction Method of Color Camera Based on LASSO Regression Model

doi:10.7517/j.issn.1674-0475.2017.02.153

Imaging Science and Photochemistry ›› 2017, Vol. 35 ›› Issue (2): 153-161.DOI: 10.7517/j.issn.1674-0475.2017.02.153

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A Color Correction Method of Color Camera Based on LASSO Regression Model

GUO Yue¹, GAO Kun¹, ZHU Jun², DOU Zeyang¹, HUANG Yadong¹, FENG Yunpeng³

1. Key Laboratory of Photoelectronic Imaging Technology and System, Ministry of Education, Beijing Institute of Technology, Beijing 100081, P. R. China;
2. Department of Precision Instrument, Tsinghua University, Beijing 100084, P. R. China;
3. Shenzhen Research Institute, Beijing Institute of Technology, Shenzhen 518057, Guangdong, P. R. China

Received:2017-01-23 Revised:2017-02-16 Online:2017-03-15 Published:2017-03-15

Abstract

Abstract:

Color correction is the essential safeguard method to keep the image color constancy for a color camera. In traditional color correction methods, the color calibration coefficients are mostly obtained by the polynomial regression model, whose correction accuracy is often insufficient. So a high order polynomial fitting method based on LASSO (Least Absolute Shrinkage and Selection Operator) regression model is proposed, considering that LASSO can compress the polynomial coefficients efficiently to guarantee the model complexity and improve the correction precision as well. The experiments are conducted using D65 standard light source and ColorChecker 24 as the imaging object. The results characterized by CIELAB color difference function show that compared with traditional linear regression and quadratic polynomial regression methods, the images corrected by LASSO method has a better effect, with the mean color difference value about 5.

Key words: color correction, LASSO regression, polynomial regression

GUO Yue, GAO Kun, ZHU Jun, DOU Zeyang, HUANG Yadong, FENG Yunpeng. A Color Correction Method of Color Camera Based on LASSO Regression Model[J]. Imaging Science and Photochemistry, 2017, 35(2): 153-161.

References

[1] Lam E Y, Fung G S K. Single-Sensor Imaging:Methods and Applications for Digital Cameras[M]. Boca Raton:CRC Press, 2008. 267-294.
[2] 董俊鹏. 基于光照分析的颜色恒常性算法研究[D].合肥工业大学, 2015. Dong J P. Research of color constancy algorithm based on illumination analysis[D]. Hefei University of Technology, 2015.
[3] 徐晓昭, 沈兰荪, 刘长江. 颜色校正方法及其在图像处理中的应用[J]. 计算机应用研究, 2008, 25(8):2250-2254. Xu X Z, Shen L S, Liu C J. Color correction methods and application in image processing[J]. Application Research of Computers, 2008, 25(8):2250-2254.
[4] 宫睿.智能移动终端图像显示性能评价方法及应用研究[D]. 浙江大学, 2014. Gong R. Study of assessment method and application for image display performance of smart mobile devices[D]. Zhejiang University, 2014.
[5] 丁二锐, 王义峰, 曾平, 丁阳. 基于结构风险最小化和全最小二乘法的色彩校正[J]. 电子与信息学报, 2008, 30(3):717-720. Ding E R, Wang Y F, Zeng P, Ding Y. Color calibration based on structural risk minimizationand total least squares[J]. Journal of Electronics & Information Technology, 2008, 30(3):717-720.
[6] 刘关松, 吕嘉雯, 徐建国, 高敦岳. 监督颜色校正方法研究[J]. 计算机学报, 2003, 26(4):502-506. Liu G S, Lv J W, Xu J G, Gao D Y. The study of supervised color correction[J]. Chinese Journal of Computers, 2003, 26(4):502-506.
[7] Kim S J, Koh K M, Lustig M, Boyd S, Gorinevsky D. An interior-point method for large-scale-regularized least squares[J]. IEEE Journal of Selected Topics in Signal Processing, 2007, 1(4):606-617.
[8] Xu Z B, Zhang H, Wang Y, Chang X Y, Liang Y. L1/2 regularization[J]. Science China Information Sciences, 2010, 53(6):1159-1169.
[9] Tibshirani R. Regression shrinkage and selection via the lasso:a retrospective[J]. Journal of the Royal Statistical Society:Series B (Statistical Methodology), 2011, 73(3):273-282.
[10] 张靖. 面向高维小样本数据的分类特征选择算法研究[D]. 合肥工业大学, 2014. Zhang J. Classification and feature selection on high-dimensional and small-sampling data[D]. Hefei University of Technology, 2014.
[11] Bradley E, Trevor H, Iain J, Tibshirani R. Least angle regression[J]. The Annals of Statistics, 2004, 32(2):407-499.
[12] 柯郑林. Lasso及其相关方法在多元线性回归模型中的应用[D]. 北京交通大学, 2011. Ke Z L. The application of Lasso and its related methods in multiple linear regression model[D]. Beijing Jiaotong University, 2011.
[13] 刘柳, 陶大程. Lasso问题的最新算法研究[J]. 数据采集与处理, 2015, 30(1):35-46. Liu L, Tao D C. Review on recent method of solving Lasso problem[J]. Journal of Data Acquisition and Processing, 2015, 30(1):35-46.
[14] 方竞宇, 徐海松, 汪哲弘, 吴晓玟. 相机参数可变的齐次多项式色度特征化模型[J]. 光学学报, 2016, 36(8):309-316. Fang J Y, Xu H S, Wang Z H, Wu X W. Homogeneous polynomial model for colorimetric characterization of camera with variable settings[J]. Acta Optica Sinica, 2016, 36(8):309-316.

A Color Correction Method of Color Camera Based on LASSO Regression Model

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