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Báo cáo hóa học: " Research Article Image Informative Maps for Estimating "

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Image Informative Maps for Estimating | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2011 Article ID 806516 12 pages doi 10.1155 2011 806516 Research Article Image Informative Maps for Estimating Noise Standard Deviation and Texture Parameters M. Uss 1 2 B. Vozel 1 V. Lukin 3 S. Abramov 3 I. Baryshev 2 and K. Chehdi1 1 TSI2M Laboratory University of Rennes 1 BP 80518 22305 Lannion cedex France 2 Department of Design of Aircraft Radio-Electronic Systems National Aerospace University Kharkov Aviation Institute 17 Chkalova Street 61070 Kharkov Ukraine 3 Department of Receivers Transmitters and Signal Processing National Aerospace University Kharkov Aviation Institute 17 Chkalova Street 61070 Kharkov Ukraine Correspondence should be addressed to B. Vozel benoit.vozel@enssat.fr Received 7 December 2010 Accepted 21 February 2011 Academic Editor Joao Manuel R. S. Tavares Copyright 2011 M. Uss et al. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. The problem of automatic detection of image areas appropriate for accurate estimation of additive noise standard deviation STD irrespectively to processed image properties is considered in this paper. For accurate estimation of either image texture or noise STD we distinguish two complementary informative maps noise- NI- and texture- TI- informative ones. The NI map is determined and iteratively upgraded based on the Fisher information on noise STD calculated in scanning window SW fashion. Fractional Brownian motion fBm model for image texture is used to derive the required Fisher information. To extract final noise STD from NI map fBm- and DCT-based estimators are implemented. The performance of these two estimators is comparatively assessed on large image database for different noise levels. It is also compared with performance of two competitive state-of-the-art .