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The article an approach to improve the accuracy of restoring the boundaries of objects obtained to create 3D structures by The paper proposes a data processing algorithm that allows performing primary processing operations in order to identify the main parameters and fusion them into a single image. To form a complex image, it is possible to first enter the parameters selected by the operator, which corrects the mixing ratio. As a noise reduction algorithm for different ranges, the multi-criteria filtering method is used, which is based on minimizing the sum of the squared deviations of the input signal and the generated estimate, as well as the sum of the squared differences of the obtained estimates. Using the adjustment factor allows you to set the degree of influence of the criterion on the resulting processing. Using this method also allows you to detect the boundaries of objects. The search for the border is based on the analysis of frequency components and the search for sharp changes in color gradation. The possibility of applying this approach for various types of data is shown on the example of processing parallel streams. For the primary construction of areas of significance, an algorithm for changing the range of clusters and an object complexity analyzer are used. The analyzer is built on the basis of calculating the weighted value of the number of color gradient transitions per unit area. To visually improve the quality of the data, a color space conversion algorithm based on alpha mixed is used. As test data used to evaluate the effectiveness, pairs of test images are used, obtained by sensors fixed at resolution of 1024x768 (8 bit, color image) and far-IR spectrum 320x240 (8 bit, color image). Images of simple shapes are used as analyzed objects.
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