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Proceedings Paper

Automatic classification for noise of infrared images into processes by means of the principal component analysis
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Paper Abstract

Noise characterization and classification is an important task to evaluate the performance of an infrared imaging system. The focal plane array infrared cameras present several types of noises: fixed pattern noise, 1/f noise, pure temporal noise, etc. The existence of bad pixels showing a singular behavior must be included in the noise description. In this paper we show how the principal component analysis is able to classify the noise of a set of frames into different subsets. The classification method is integrated into a software package that performs the classification of the obtained eigenimages into processes. This method is specially adapted to the analysis of noise in a set of frames because it produces a corresponding set of images characterizing the noise. A result of the analysis provided with this method is the extraction of the fixed pattern noise, the bad pixel identification, the 1/f nosie components and analysis, the pure temporal noise, and some other processes having intermediate time scales.

Paper Details

Date Published: 29 July 2002
PDF: 12 pages
Proc. SPIE 4719, Infrared and Passive Millimeter-wave Imaging Systems: Design, Analysis, Modeling, and Testing, (29 July 2002); doi: 10.1117/12.477455
Show Author Affiliations
Jose Manuel Lopez-Alonso, Univ. Complutense de Madrid (Spain)
Javier Alda, Univ. Complutense de Madrid (Spain)


Published in SPIE Proceedings Vol. 4719:
Infrared and Passive Millimeter-wave Imaging Systems: Design, Analysis, Modeling, and Testing
Roger Appleby; David A. Wikner; Roger Appleby; Gerald C. Holst; David A. Wikner, Editor(s)

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