吉林大学学报(理学版) ›› 2022, Vol. 60 ›› Issue (6): 1377-1382.

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一种基于主成分分析的图像光照校正算法

张震1, 赵启鹏1, 朱留存1, 刘济尘2, 罗俊琦1, 魏金占1, 李雪3   

  1. 1. 北部湾大学 先端科学技术研究院, 广西 钦州 535001; 
    2. 吉林大学 软件学院, 长春 130012;  3. 中国农业科学院 长春兽医研究所, 长春 130118
  • 收稿日期:2022-01-04 出版日期:2022-11-26 发布日期:2022-11-26
  • 通讯作者: 赵启鹏 E-mail:348533483@qq.com

An Image Illumination Correction Algorithm Based on Principal Component Analysis

ZHANG Zhen1, ZHAO Qipeng1, ZHU Liucun1, LIU Jichen2, LUO Junqi1, WEI Jinzhan1, LI Xue3   

  1. 1. Advanced Science and Technology Research Institute, Beibu Gulf University, Qinzhou 535001, Guangxi Zhuang Autonomous Region, China;
    2. College of Software, Jilin University, Changchun 130012, China; 3. Changchun Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Changchun 130018, China
  • Received:2022-01-04 Online:2022-11-26 Published:2022-11-26

摘要: 针对受相同光照条件干扰图像的光照校正问题, 提出一种基于主成分分析的图像光照校正算法. 首先, 利用给定的多张图像构造协方差矩阵, 并计算特征值和特征向量; 然后, 根据特征向量构造一组标准正交基; 最后, 选择部分基底(主成分)重构图像. 实验结果表明, 该算法能很好地消除光照影响, 同时保留图像的主要特征.  与经典算法进行对比验证了该算法的有效性.

关键词: 主成分分析, 图像处理, 光照校正, 特征向量

Abstract: Aiming at the  illumination correction problem of the image interfered by the same illumination conditions, we proposed an image illumination correction algorithm based on principal component analysis.  Firstly, a covariance matrix was constructed by using given multiple images, and the eigenvalues and eigenvectors were calculated. Secondly,  a set of normal orthogonal bases was constructed according to  the eigenvectors. Finally, a part of substrate principal component was selected to reconstruct the image. 
 The experimental results show that the algorithm can eliminate the influence of illumination and retain the main features of the image.  Compared with the  classical algorithm,  the effectiveness of the algorithm is verified.

Key words: principal component analysis, image processing, illumination correction, eigenvector

中图分类号: 

  • TP391.41