Research Article
Automatic Color Control Method of Low Contrast Image Based on Big Data Analysis
@INPROCEEDINGS{10.1007/978-3-030-36405-2_19, author={Jia Wang and Zhiqin Yin and Xiyan Xu and Jianfei Yang}, title={Automatic Color Control Method of Low Contrast Image Based on Big Data Analysis}, proceedings={Advanced Hybrid Information Processing. Third EAI International Conference, ADHIP 2019, Nanjing, China, September 21--22, 2019, Proceedings, Part II}, proceedings_a={ADHIP PART 2}, year={2019}, month={11}, keywords={Big data analysis Low contrast image Fusion Color automatic control}, doi={10.1007/978-3-030-36405-2_19} }- Jia Wang
Zhiqin Yin
Xiyan Xu
Jianfei Yang
Year: 2019
Automatic Color Control Method of Low Contrast Image Based on Big Data Analysis
ADHIP PART 2
Springer
DOI: 10.1007/978-3-030-36405-2_19
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.
