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Principal-component characterization of noise for infrared images

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López Alonso, José Manuel and Alda, Javier and Bernabeu Martínez, Eusebio (2002) Principal-component characterization of noise for infrared images. Applied Optics, 41 (2). pp. 320-331. ISSN 1559-128X

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Official URL: http://dx.doi.org/10.1364/AO.41.000320


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Abstract

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.


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© Optical Society of America.
This research has been developed within the collaboration program between the Centro de Investigación y Desarrollo de la Armada (CIDA) and the Optics Department of the University Complutense of Madrid. The authors are deeply grateful to Benjamín M. Alvariño, director of CIDA when this research began, and to Felipe López-Merenciano, head of the Thermovision Laboratory at CIDA.

Uncontrolled Keywords:Optics
Subjects:Sciences > Physics > Optics
ID Code:26780
Deposited On:25 Sep 2014 10:50
Last Modified:25 Sep 2014 10:50

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