Paper Info
Title
Automatic Color Control Method Of Low Contrast Image Based On Big Data Analysis
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.
Year
DOI
Venue
2019
10.1007/978-3-030-36405-2_19
ADVANCED HYBRID INFORMATION PROCESSING, ADHIP 2019, PT II
Keywords
DocType
Volume
Big data analysis, Low contrast image, Fusion, Color automatic control
Conference
302
ISSN
Citations 
PageRank 
1867-8211
0
0.34
References 
Authors
0
4
Name
Order
Citations
PageRank
Jia Wang100.68
Zhiqin Yin200.34
Xiyan Xu300.34
Jianfei Yang422123.81