Principal-component characterization of noise for infrared images

被引:76
作者
López-Alonso, JM
Alda, J
Bernabéu, E
机构
[1] Ctr Invest & Desarrollo Armada, Lab Termovis, Madrid 28040, Spain
[2] Univ Complutense Madrid, Dept Opt, Madrid 28037, Spain
[3] Univ Complutense Madrid, Fac Fis, Dept Opt, E-28040 Madrid, Spain
关键词
D O I
10.1364/AO.41.000320
中图分类号
O43 [光学];
学科分类号
070207 ; 0803 ;
摘要
Principal-component decomposition is applied to the analysis of noise for infrared images. It provides a set of eigenimages, the principal components, that represents spatial patterns associated with different types of noise. We provide a method to classify the principal components into processes that explain a given amount of the variance of the images under analysis. Each process can reconstruct the set of data, thus allowing a calculation of the weight of the given process in the total noise. The method is successfully applied to an actual set of infrared images. The extension of the method to images in the visible spectrum is possible and would provide similar results. (C) 2002 Optical Society of America.
引用
收藏
页码:320 / 331
页数:12
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