Igor Baryshev

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A novel approach to joint blind estimation of additive noise variance and probability of impulsive noise occurrence in images is put forward. It is based on using a fractal Brownian model for description of real life image characteristics. As shown, this approach allows rather accurate estimation of mixed noise parameters even for images containing a large(More)
The problem of automatic detection of image areas that can be reliably selected for accurate estimation of additive noise standard deviation (STD), irrespectively to processed image properties, is considered in this paper. For getting accurate estimate of either texture or noise parameters involved, we distinguish two complementary image informative maps:(More)
We analyze applicability of 2D fractal Brownian motion (fBm) for real-life image textures with respect to two general fBm properties: isotropy and normality of its increments. A non-parametric detection scheme for texture satisfying these two properties is proposed. It is based on Lilliefors test for texture increments normality and Kolmogorov-Smirnov two(More)
The maximally realistic method of estimation of higher derivatives of Doppler frequency on the basis of coherent pulse group due to its partitioning has been developed. Dependencies of time of optimization and probability of anomalous errors as functions of a number of fragments, parameters of the pulse group and signal-to-noise ratio have been obtained.(More)
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