24 October 2007 Improved minimal inter-quantile distance method for blind estimation of noise variance in images
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Multichannel (multi and hyperspectral, dual and multipolarization, multitemporal) remote sensing (RS) is widely used in different applications. Noise is one of the basic factors that deteriorates RS data quality and prevents retrieval of useful information. Because of this, image pre-filtering is a typical stage of multichannel RS data pre-processing. Most efficient modern filters and other image processing techniques employ a priori information on noise type and its statistical characteristics like variance. Thus, there is an obvious need in automatic (blind) techniques for determination of noise type and its characteristics. Although several such techniques have been already developed, not all of them are able to perform appropriately in cases when considered images contain a large percentage of texture regions and other locally active areas. Recently we have designed a method of blind determination of noise variance based on minimal inter-quantile distance. However, it occurred that its accuracy could be further improved. In this paper we describe and analyze several ways to do this. One opportunity deals with better approximation of inter-quantile distance curve. Another opportunity concerns the use of image pre-segmentation before forming an initial set of local estimates of noise variance. Both ways are studied for model data and test images. Numerical simulation results confirm improvement of estimate accuracy for the proposed approach.
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Vladimir V. Lukin, Vladimir V. Lukin, Sergey K. Abramov, Sergey K. Abramov, Alexander A. Zelensky, Alexander A. Zelensky, Jaakko T. Astola, Jaakko T. Astola, Benoit Vozel, Benoit Vozel, Kacem Chehdi, Kacem Chehdi, "Improved minimal inter-quantile distance method for blind estimation of noise variance in images", Proc. SPIE 6748, Image and Signal Processing for Remote Sensing XIII, 67481I (24 October 2007); doi: 10.1117/12.738006; https://doi.org/10.1117/12.738006


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