Publication: Principal-component characterization of noise for infrared images
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2002-01-10
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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.
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1. J. M. Mooney, “Effect of spatial noise on the minimum resolvable temperature of a staring sensor”, Appl. Opt. 30, 3324–3332 (1991).
2. C. Webb, J. A. D’Agostino, “Manual reference of FLIR 92”, (U.S. Army Night Vision and Electronic Sensor Directorate, Washington, D.C., 1992).
3. H. Rothe, A. Duparr, S. Jacobs, “Generic detrending of surface profiles”, Opt. Eng. 33, 3023–3030 (1994).
4. H. Rothe, M. Tuerschmann, P. Mager, R. Endter, “Improved accuracy in laser triangulation by variance-stabilizing transformations”, Opt. Eng. 31, 1538–1545 (1992).
5. H. Rothe, O. Ginter, C. Woldenga, “Assessment and robust reconstruction of laser radar signals”, Opt. Laser Technol. 25, 289–297 (1993).
6. G. Rasigni, F. Varnier, M. Rasigni, J. P. Palmari, “Autocovariance functions for polished optical surfaces”, J. Opt. Soc. Am. 73, 222–233 (1983).
7. D. F. Morrison, Multivariate Statistical Methods, 3rd ed. (McGraw-Hill, Singapore, 1990), Chap. 8.
8. R. B. Cattell, “The scree test for the number of factors”, J. Multivar. Behav. Res. 1, 245–276 (1966).
9. G. R. North, T. L. Bell, R. F. Cahalan, F. J. Moeng, “Sampling errors in the estimation of empirical orthogonal functions”, Mon. Weather Rev. 19, 699–706 (1982).
10. C. L. Bennett, “The effect of jitter on an imaging FTIR spectrometer”, in Infrared Imaging Systems: Design, Analysis, Modeling and Testing VIII, C. Holst, ed., Proc. SPIE3063, 174–184 (1997).
11. H. Rothe, H. Truckenbrodt, “Discrimination of surface properties using BRDF-variance estimators as feature variables”, in Specification and Measurement of Optical Systems, L. R. Baker, ed., Proc. SPIE1781, 152–162 (1992).
12. S. Hare, “Low frequency climate variability and salmon production”, Ph.D. dissertation (University of Washington, Seattle, Washington, 1996), Chap. 1. See http://www.iphc.washington.edu/Staff/hare/html/diss/chapter1/chap1.html .
13. C. M. Waternaux, “Asymptotic distribution of the sample roots for a nonnormal population”, Biometrika 63, 639–645 (1976).
14. A. W. Davis, “Asymptotic theory for principal component analysis: non-normal case”, Aust. J. Stat. 19, 206–212 (1977).