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Proceedings Paper

A method for blind automatic evaluation of noise variance in images based on bootstrap and myriad operations
Author(s): Vladimir V. Lukin; Sergey K. Abramov; Benoit Vozel; Kacem Chehdi
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Paper Abstract

Multichannel (multispectral) remote sensing (MRS) is widely used for various applications nowadays. However, original images are commonly corrupted by noise and other distortions. This prevents reliable retrieval of useful information from remote sensing data. Because of this, image pre-filtering and/or reconstruction are typical stages of multichannel image processing. And majority of modern efficient methods for image pre-processing requires availability of a priori information concerning noise type and its statistical characteristics. Thus, there is a great need in automatic blind methods for determination of noise type and its characteristics. However, almost all such methods fail to perform appropriately well if an image under consideration contains a large percentage of texture regions, details and edges. In this paper we demonstrate that by applying bootstrap it is possible to obtain rather accurate estimates of noise variance that can be used either as the final or preliminary ones. Different quantiles (order statistics) are used as initial estimates of mode location for distribution of noise variance local estimations and then bootstrap is applied for their joint analysis. To further improve accuracy of noise variance estimations, it is proposed under certain condition to apply myriad operation with tunable parameter k set in accordance with preliminary estimate obtained by bootstrap. Numerical simulation results confirm applicability of the proposed approach and produce data allowing to evaluate method accuracy.

Paper Details

Date Published: 19 October 2005
PDF: 12 pages
Proc. SPIE 5982, Image and Signal Processing for Remote Sensing XI, 59820Y (19 October 2005); doi: 10.1117/12.626446
Show Author Affiliations
Vladimir V. Lukin, National Aerospace Univ. (Ukraine)
Sergey K. Abramov, National Aerospace Univ. (Ukraine)
Benoit Vozel, Univ. de Rennes I (France)
Kacem Chehdi, Univ. de Rennes I (France)


Published in SPIE Proceedings Vol. 5982:
Image and Signal Processing for Remote Sensing XI
Lorenzo Bruzzone, Editor(s)

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