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Automatic Color Control Method of Low Contrast Image Based on Big Data Analysis

  • Jia WangEmail author
  • Zhiqin Yin
  • Xiyan Xu
  • Jianfei Yang
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 302)

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.

Keywords

Big data analysis Low contrast image Fusion Color automatic control 

Notes

Acknowledgement

High Level Backbone Major of Higher Vocational Education in Yunnan Province——Construction Project of Major in Print Media Technology.

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Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2019

Authors and Affiliations

  1. 1.Mechanical Engineering CollegeYunnan Open UniversityKunmingChina

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