Principal-component characterization of noise for infrared images
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2002
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The Optical Society Of America
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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.