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25 August 2006 Use of minimal inter-quantile distance estimation in image processing
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Nowadays multichannel (multi and hyperspectral) remote sensing (RS) is widely used in different areas. One of the basic factors that can deteriorate original image quality and prevent retrieval of useful information from RS data is noise. Thus, image filtering is a typical stage of multichannel image pre-processing. Among known filters, the most efficient ones commonly require a priori information concerning noise type and its statistical characteristics. This explains a great need in automatic (blind) methods for determination of noise type and its characteristics. Several such methods already exist, but majority of them do not perform appropriately well if analyzed images contain a large percentage of texture regions, details and edges. Besides, many blind methods are multistage where some preliminary and appropriately accurate estimate of noise variance is required for next stages. To get around aforementioned shortcomings, below we propose a new method based on using inter-quantile distance and its minimization for obtaining appropriately accurate estimates of noise variance. It is shown that mathematically this task can be formulated as finding a mode of contaminated asymmetric distribution. And this task can be met for other applications. The efficiency of the proposed method is studied for a wide set of model distribution parameters. Numerical simulation results that confirm applicability of the proposed approach are presented. They also allow evaluating the designed method accuracy. Recommendations on method parameter selection are given.
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Vladimir V. Lukin, Sergey K. Abramov, Alexander A. Zelensky, and Jaakko T. Astola "Use of minimal inter-quantile distance estimation in image processing", Proc. SPIE 6315, Mathematics of Data/Image Pattern Recognition, Compression, and Encryption with Applications IX, 63150O (25 August 2006);

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