A.I. Novikov - Ph. D. (Eng.), Associate Professor, Department «Higher Mathematics», Ryazan State Radio Engineering University E-mail: novikovanatoly@yandex.ru D.I. Ustyukov - Engineer, Department of Computer, Ryazan State Radio Engineering University E-mail: ustukov.mail@yandex.ru

In the article under consideration as a source of information are the noisy images obtained from different spectral sensors in machine vision systems. However, a noise suppression problem arises in digital data processing systems in different transmit and receive in-formation. The results of this research are quite general and applies to all system of reception, transmission and processing of infor-mation. First the most detailed research of smoothing properties of operators with finite memory for noise suppression in one-dimension (time series) was introduced in books [1, 2]. As known in a small neighborhood of any smooth function can be well approximated by a polynomial of finite degree. Therefore, in the [1.2] considered smoothing operators having the property of unbiased estimation of polynomials any, prescribed degree at a predetermined interval of finite length. There was made reasonable assumption about the ability of smoothing operators creation with the same properties in the two-dimensional case in [3]. The corresponding matrix masks have been proposed. Questions about two-dimensional image smoothing were viewed in many domestic and foreign monographs and textbooks, such as [4−7]. However, some aspects of the problem, continue to explore today. The detail research of four matrix operator types by three criteria - the level of noise reduction, the degree of distortion of the low frequency component and the time required to perform the algorithm. The article shows that not only the power of noise reduction must be considered in choosing an operator but also the power of it low frequency component distortion. Otherwise the smoothing operator may distort the informative part of the image so much that it will affect the quality of the following image processing tasks. Many operators with high noise reduction coefficient values but with non-integer matrix mask elements can be approximated by mask with integer elements, whose smoothing properties are close to the same properties of the main matrix operator. Concluded that the best in average of three indicators - noise suppression level, distortion of the low frequency component and com-putational costs of the algorithm. among operators with 2 dimensional mask with the same size are matrix mask operators with power of two elements.

 

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