Home – Home – Jornals – Advanced Search

A new algorithm for processing a series of images with a low sampling frequency, aimed at forming high-resolution images, is proposed. The algorithm is based on the use of a linear filter with a limited aperture calculated by the method of optimal linear prediction. Experimental estimates of the quality of high-resolution image reconstruction are given for different values of the filter size, number of initial images, and level of accompanying additive noise.

Some modifications of the algorithm of image construction with a high sampling frequency are proposed. These modifications multiply reduce the computational costs with an almost unchanged quality of the result. Software implementation of the proposed modifications of the algorithm allows the latter to be used in video surveillance systems for real-time formation of high-quality image fragments.

A new algorithm of estimating the stationary part of the background of the observed scene and its adaptation to changes in this scene and observation conditions are proposed. The algorithm is optimal in the statistical sense and employs the minimum number of statistical characteristics (moving average, variance, and median), which allows their recurrent calculation necessary for real-time implementation. The validity of the proposed method is confirmed by results of a computational experiment with real video sequences.

A possibility of increasing the resolution of photodetector arrays by doubling the spatial sampling frequency and subsequent processing of the samples obtained is considered.

An improved algorithm is proposed to calculate the mutual correlation functions (MCFs) of signals using the fast Fourier transform, the quadrature formulas of the trapezoidal method, and the Simpson method. The calculation error of MCFs is analyzed by comparing MCFs obtained for continuous finite signal samples and MCFs obtained using improved calculation algorithms for discrete signal samples. It is shown that these algorithms for calculating MCFs provide a more accurate primary estimate of MCFs. Improved calculation algorithms are described.

An algorithm of reconstruction of two-dimensional radar images in a frequency range is proposed. The algorithm is based on the property of the instrument function of separation of variables, which allows the procedure of two-dimensional reconstruction to be reduced to a two-stage procedure of one-dimensional reconstruction, thus, ensuring faster performance. A comparison with the algorithm of reconstruction in a spatial domain is presented.

The process of constructing vector fields of displacements of clouds in the atmosphere from a series of consecutive images obtained by geostationary satellites is considered. The process is based on searching for the maximum of the coefficient of mutual correlation between reference objects (targets) found in the current image of the series and their positions in the next image of the series. In addition to the transfer transformation, the target can also be subjected to transformations of rotation and scaling. Interpolation of pixels is used to increase the accuracy of computations.

The estimation of the characteristics of motion of the object controllable by means of seismic monitoring system is considered. It is assumed that the motion in a local area of space is uniform and rectilinear, and the problem is therefore reduced to estimating the coordinates of the initial point and velocity vector. The input data are measured time delay differences in receiving seismic signals by various sensors of the system. Usually, these measurements are formed as a sequential flow in real systems. The obtained estimates of the trajectory also have the form of a sequence, and their accuracy increases with increasing number of primary data. The accuracy of the proposed method are analyzed by statistical simulation.

X-ray radiography is extended by a digital image processing method for measuring the velocity vector field of metal foaming in radiographic sequences. A tensor approach coupled with interpolation using an adaptive Gaussian window is proposed. The method was successfully tested on experimental images.

Statistically optimal and neural network algorithms of joint detection and estimation of brightness difference parameters in a local observation window are synthesized for the purpose of solving the problem of object boundary extraction in digital images in the presence of noise. Comparative results on the performance of these algorithms are given.