文献类型：期刊文献

中文题名：Color Estimation for Thermal Infrared Imagery Based on Kernel PCA and Sparse Representation

英文题名：Color estimation for thermal infrared imagery based on kernel PCA and sparse representation

作者：Sun, Shao-Yuan[1,2]; Zhao, Hai-Tao[3]; Gu, Xiao-Jing[3]

机构：[1] College of Information Science and Technology, Donghua University, Shanghai 201620, China; [2] Engineering Research Center of Digital Textile and Fashion Technology, Ministry of Education, Donghua University, Shanghai 201620, China; [3] Automation Department, East China University of Science and Technology, Shanghai 200237, China

年份：2012

卷号：29

期号：6

起止页码：475

中文期刊名：Journal of Donghua University(English Edition)

外文期刊名：Journal of Donghua University (English Edition)

收录：EI(收录号：20131716238502);Scopus

基金：National Natural Science Foundation of China(No. 61072090);the Fundamental Research Funds for the Central Universities,China;Shanghai Pujiang Program,China(No. 12PJ1402200);China Postdoctoral Science Foundation Funded Project(No. 2012M511058);Shanghai Postdoctoral Sustentation Fund,China(No. 12R21412500)

语种：英文

中文关键词：color night vision; infrared image rendering; kernelprincipal component analyst＇s （KPCA） ; sparse representation

外文关键词：Color - Rendering (computer graphics) - Principal component analysis - Infrared imaging - Image analysis

摘要：Adding colors to monochrome thermal infrared images can help observers understand the scenery better. A nonlinear color estimation method for single-band thermal infrared imagery based on kernel principal component analysis (KPCA) and sparse representation was proposed. Nonlinear features of infrared image were extracted using KPCA. The relationship between image features and chromatic values was learned using sparse representation and a color estimation model was obtained. The thermal infrared images can be rendered automatically using the color estimation model. The experimental results show that the proposed method can render infrared image with an accurate color appearance. The proposed idea can also be used in other color estimation problem.
Adding colors to monochrome thermal infrared images can help observers understand the scenery better. A nonlinear color estimation method for single-band thermal infrared imagery based on kernel principal component analysis (KPCA) and sparse representation was proposed. Nonlinear features of infrared image were extracted using KPCA. The relationship between image features and chromatic values was learned using sparse representation and a color estimation model was obtained. The thermal infrared images can be rendered automatically using the color estimation model. The experimental results show that the proposed method can render infrared image with an accurate color appearance. The proposed idea can also be used in other color estimation problem. ? 2012 by Editorial Board of Journal of Donghua University, Shanghai, China.