Abstract
In order to improve the imaging quality of 3D image with visual feature reconstruction, it is necessary to control the color of low contrast image automatically. A color automatic control technology of low contrast image based on 3D color space packet template feature detection is proposed, the automatic color control model of image based on big data analysis is constructed. RGB decomposition technology is used to extract the color components of low contrast images, and color space gray feature fusion algorithm is used to segment fusion of low contrast images to improve the feature pairing performance of color peak points of low contrast images. Combined with the color space block fusion information of low contrast image, the edge features of high oscillatory region are detected, and the color automatic control of low contrast image is realized. The simulation results show that the color automatic control of low contrast image can improve the peak signal-to-noise ratio (PSNR) of image output, improve the automatic color control ability and imaging quality of low contrast image.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Alfaro, V.M., Vilanovab, R.: Robust tuning of 2DoF five-parameter PID controllers for inverse response controlled processes. J. Process Control 23(4), 453–462 (2013)
Yu, M., Zhang, H.: HDR imaging based on low-rank matrix completion and total variation constraint. Comput. Eng. 45(4), 262–266 (2019). 274
Dai, S., Lü, K., Zhai, R., Dong, J.: Lung segmentation method based on 3D region growing method and improved convex hull algorithm. J. Electron. Inf. 38(9), 2358–2364 (2016)
Yang, J., Zhao, J., Qiang, Y., et al.: Lung CT image segmentation combined multi-scale watershed method and region growing method. Comput. Eng. Design 35(1), 213–217 (2014)
Jiang, Z., Cheng, C.: Improved HOG face feature extraction algorithm based on haar characteristics. Comput. Sci. 44(1), 303–307 (2017)
Li, G., Li, H., Shang, F., Guo, H.: Noise image segmentation model with local intensity difference. J. Comput. Appl. 38(3), 842–847 (2018)
Shan, Y., Wang, J.: Robust object tracking method of adaptive scale and direction. Comput. Eng. Appl. 54(21), 208–216 (2018)
Dai, H., Huang, Y., Li, C., et al.: Energy-efficient resource allocation for device-to-device communication with WPT. IET Commun. 11(3), 326–334 (2017)
Ma, Z., Chen, W.: Friction torque calculation method of ball bearings based on rolling creepage theory. J. Mech. Eng. 53(22), 219–224 (2017)
Zhou, S.B., Xu, W.X.: A novel clustering algorithm based on relative density and decision graph. Control Decis. 33(11), 1921–1930 (2018)
Acknowledgement
High Level Backbone Major of Higher Vocational Education in Yunnan Province——Construction Project of Major in Print Media Technology.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Wang, J., Yin, Z., Xu, X., Yang, J. (2019). Automatic Color Control Method of Low Contrast Image Based on Big Data Analysis. In: Gui, G., Yun, L. (eds) Advanced Hybrid Information Processing. ADHIP 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 302. Springer, Cham. https://doi.org/10.1007/978-3-030-36405-2_19
Download citation
DOI: https://doi.org/10.1007/978-3-030-36405-2_19
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-36404-5
Online ISBN: 978-3-030-36405-2
eBook Packages: Computer ScienceComputer Science (R0)