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We consider the problem of calculating a refractive element with two surfaces, forming a flat front and a given distribution of illumination. The supporting quadrics method is formulated for calculating a given optical element and it is shown that this method coincides with the gradient method for some functional related to the problem of the Monge-Kantorovich mass transfer problem. This enables adaptive selection of the step in the supporting quadric method. At the end of the article a design example is given.

The use of neural networks to detect differences in radiographic images of patients with pneumonia and COVID-19 is demonstrated For the optimal selection of resize and neural network architecture parameters, hyperparameters, and adaptive image brightness adjustment, precision, re-call, and f1-score metrics are used The high values of these metrics of classification quality (> 0 91) strongly indicate a reliable difference between radiographic images of patients with pneumonia and patients with COVID-19, which opens up the possibility of creating a model with good predictive ability without involving ready-to-use complex models and without pre-training on third-party data, which is promising for the development of sensitive and reliable COVID-19 ex-press-diagnostic methods © 2021, Institution of Russian Academy of Sciences All rights reserved

For non-contact 3D measurements in hydrodynamic tunnels by photogrammetry methods, it is necessary to refine the standard model of image formation in the camera by taking into account an effect of refraction of rays at the boundaries of optical media, namely, at an air-glass boundary and glass-working fluid boundary. The article presents a model of image formation for shooting in a working environment that includes various optical media and methods for calibrating an optical system for 3D measurements of the coordinates of scene objects, while taking into account the real boundaries of the optical media. Experimental results on calibrating the system of three-dimensional measurements when an object image is formed by rays passing through two optical boundaries are discussed.

With the development of economy, the distribution problem of logistics becomes more and more complex. Based on the traffic network data, this study analyzed the vehicle routing problem (VRP), designed a dynamic vehicle routing problem with time window (DVRPTW) model, and solved it with genetic algorithm (GA). In order to improve the performance of the algorithm, the genetic operation was improved, and the output solution was further optimized by hill climbing algorithm. The analysis of example showed that the improved GA algorithm had better performance in path optimization planning, the total cost of planning results was 31.44 % less than that of GA algorithm, and the total cost of planning results increased by 11.48 % considering the traffic network data. The experimental results show that the improved GA algorithm has good performance and can significantly reduce the cost of distribution and that research on VRP based on the traffic network data is more in line with the actual situation of logistics distribution, which is conducive to the further application of the improved GA algorithm in VRP.

Melanoma skin cancer is one of the most dangerous forms of skin cancer because it grows fast and causes most of the skin cancer deaths. Hence, early detection is a very important task to treat melanoma. In this article, we propose a skin lesion segmentation method for dermoscopic images based on the U-Net architecture with VGG-16 encoder and the semantic segmentation. Base on the segmented skin lesion, diagnostic imaging systems can evaluate skin lesion features to classify them. The proposed method requires fewer resources for training, and it is suitable for computing systems without powerful GPUs, but the training accuracy is still high enough (above 95 %). In the experiments, we train the model on the ISIC dataset – a common dermoscopic image dataset. To assess the performance of the proposed skin lesion segmentation method, we evaluate the Sorensen-Dice and the Jaccard scores and compare to other deep learning-based skin lesion segmentation methods. Experimental results showed that skin lesion segmentation quality of the proposed method are better than ones of the compared methods.

In this work, the splitting of resonance lines in a Fabry-Perot resonator formed by a section of a standard curved single-mode fiber with metal-coated ends is investigated numerically and experimentally. It is shown that this splitting is similar to the Autler-Townes splitting and results from a strong coupling between the fundamental mode of the core and the whispering gallery mode of the cladding of the curved fiber. The influence of all basic parameters of the curved resonator on the splitting of its resonance lines in the reflection and transmission spectra is considered. Prospects for the practical application of the effects studied for high-resolution optical refractometry, as well as the direction of further research are outlined.

Many recent studies have focused on developing image reconstruction algorithms in optical systems based on flat optics. These studies demonstrate the feasibility of applying a combination of flat optics and the reconstruction algorithms in real vision systems. However, additional causes of quality loss have been encountered in the development of such systems. This study investigates the influence on the reconstructed image quality of such factors as limitations of mass production technology for diffractive optics, lossy video stream compression artifacts, and specificities of a neural network approach to image reconstruction. The paper offers an end-to-end deep learning-based image reconstruction framework to compensate for the additional factors of quality losing. It provides the image reconstruction quality sufficient for applied vision systems.

Optical character recognition (OCR) in images captured from arbitrary angles requires preliminary normalization, i.e. a geometric transformation resulting in an image as if it was captured at an angle suitable for OCR. In most cases, a surface containing characters can be considered flat, and a pinhole model can be adopted for a camera. Thus, in theory, the normalization should be projective. Usually, the camera optical axis is approximately perpendicular to the document surface, so the projective normalization can be replaced with an affine one without a significant loss of accuracy. An affine image transformation is performed significantly faster than a projective normalization, which is important for OCR on mobile devices. In this work, we propose a fast approach for image normalization. It utilizes an affine normalization instead of a projective one if there is no significant loss of accuracy. The approach is based on a proposed criterion for the normalization accuracy: root mean square (RMS) coordinate discrepancies over the region of interest (ROI). The problem of optimal affine normalization according to this criterion is considered. We have established that this unconstrained optimization is quadratic and can be reduced to a problem of fractional quadratic functions integration over the ROI. The latter was solved analytically in the case of OCR where the ROI consists of rectangles. The proposed approach is generalized for various cases when instead of the affine transform its special cases are used: scaling, translation, shearing, and their superposition, allowing the image normalization procedure to be further accelerated.

Here we show theoretically that a superposition of two Bessel-Gaussian (BG) beams with different topological charges (TC) and different scaling factors (radial components of the wave vectors) has the TC equal to that of the BG beam with the larger scaling factor. If the scaling factors of the BG beams are equal, then TC of the whole superposition equals TC of the BG beam with the larger (in absolute value) weight coefficient in the superposition (i.e. with larger power). If the constituent BG beams are also same-power, TC of the superposition equals the average TC of the two BG beams. Therefore, if the sum of TCs of both beams is odd, TC of the superposition is a half-integer number. In practice, however, TC is calculated over a finite radius circle and, hence, the half-integer TC for the degenerated case cannot be obtained. Instead of the half-integer TC, the lower of the two integer TCs is obtained. Numerical simulation reveals that if the weight coefficients in the superposition are slightly different, TC of the superposition is not conserved on propagation. In the near field and in the Fresnel diffraction zone, TC is equal to the highest TC of the two BG beams, while in the far field it is equal to the lower TC. What is more, TC changes its value from high to low not instantly, but continuously at some propagation distance. In the intermediate zone TC is fractional.

The scope of uses of automated document recognition has extended and as a result, recognition techniques that do not require specialized equipment have become more relevant. Among such techniques, document recognition using mobile devices is of interest. However, it is not always possible to ensure controlled capturing conditions and, consequentially, high quality of input images. Unlike specialized scanners, mobile cameras allow using a video stream as an input, thus obtaining several images of the recognized object, captured with various characteristics. In this case, a problem of combining the information from multiple input frames arises. In this paper, we propose a weighing model for the process of combining the per-frame recognition results, two approaches to the weighted combination of the text recognition results, and two weighing criteria. The effectiveness of the proposed approaches is tested using datasets of identity documents captured with a mobile device camera in different conditions, including perspective distortion of the document image and low lighting conditions. The experimental results show that the weighting combination can improve the text recognition result quality in the video stream, and the per-character weighting method with input image focus estimation as a base criterion allows one to achieve the best results on the datasets analyzed.

We show that a 70-nm dielectric nanoparticle placed on the optical axis near the surface (at a distance less than 100 nm) of a high-NA gradient microlens made of silicon, which is illuminated by a laser beam of 1.55 μm wavelength, is attracted to the lens surface with a piconewton force. The profile of the lens refractive index is described by a hyperbolic secant function. If a cut-out is made in the lens output surface, then the nanoparticle will be pulled into this cut-out, producing a kind of 'optical magnet'. If a reverse energy flow is to be generated on the optical axis near the output surface of such a gradient lens, this will lead to an absorbing dielectric nanoparticle being pulled toward the surface with a greater force than a similar non-absorbing particle. In the absence of a reverse flow, both absorbing and non-absorbing particles will be attracted to the surface with an equal force. The electromagnetic fields involved are calculated using a finite difference time domain (FDTD) method and the acting forces are calculated using a Maxwell stress tensor.

We propose a new approach to non-contact recording of respiratory function based on the analysis of a sequence of Infrared-terahertz images of the human face, allowing the processes that occur during breathing to be visualized. To obtain quantitative estimates of the respiratory function, two methods are proposed. The first one utilizes a probe which implements the function of spatial differentiation and provides high sensitivity, but requires an increased accuracy of positioning the probe at the nasal opening and scaling in accordance with the camera angle. The other one is a histogram method for obtaining quantitative estimates of the external respiratory function, which is scale invariant and does not require precise positioning, but has a lower sensitivity compared to the first one. The methods proposed have made it possible to remotely evaluate the respiratory rate, which correlates well with the data obtained by a contact method of respiratory function registration.

We investigate a multispiral phase plate (MSPP) with multiple centers of phase singularity arbitrarily located in the MSPP plane. Equations to describe the topological charge of an optical vortex in the initial plane immediately behind the MSPP and orbital angular momentum (OAM) normalized relative to the beam power are derived. The topological charge in the initial plane is found as a sum of the topological charges of all singularities if their centers are located inside a finite-radius circular aperture. If the phase singularity centers are partially located on the boundary of a circular diaphragm limiting the MSPP, the total topological charge is found as the sum of all singularities divided by 2. Total OAM that the vortex carries depends on the location of the singularity centers: the farther from the center of the plate the singularity center is located, the smaller is its contribution to the OAM. If all singularity centers are located on the boundary of the diaphragm limiting MSPP, then the OAM of the vortex beam equals zero, although in this case the topological charge of the beam is nonzero.

Classification of the land cover types from multi- and hyperspectral (HS) imagery is traditionally carried out on the basis of analysis of scatter plots of pixel values in a multidimensional feature space, which are used as brightness in individual channels. To increase the reliability of HS image classification, approaches are used based on simultaneously accounting for the characteristics of each pixel and the nearest-neighbor pixels, i.e., on the joint analysis of spectral and spatial features. The pixel neighborhood analysis is performed at various stages of the classification process. In this work, using a test hyperspectral image, the efficiency of spectral-spatial data classification methods that take into account spatial information at various stages of processing is studied. Special attention is paid to selecting the size of the spatial processing core. It is shown that the best results are obtained by combining pre-processing of raw data before performing the procedures of pixel-by-pixel spectral classification and post-processing of the resulting maps. Prospects of multi-scale smoothing of initial images, with the increase of the number of spectral-spatial features being multiple of the number of the scales, are shown.

The buildings are very complex for detection on SAR images, where the basic features of those are shadows. There are many different representations for SAR shadow. As result it is no possible to use convolutional neural network for building detection directly. In this article we give property analysis of SAR shadows of different type buildings. After that, each region (ROI) prepared for training of building detection is corrected with its own SAR shadow properties. Reconstructions of ROI will be put in a modified YOLO network for building detection with better quality result.

The numerical simulation and selection of optimal parameters of the diffraction grating for a newly developed technology for the manufacture of plane waveguide systems are performed. In contrast to the use of ready-made silicon wafers on an insulator, the new technology has been developed for the manufacture of a fully autonomous radiation input system, a coupling element and the waveguide itself. A general description of the technology of the ‘radiation input – propagation – radiation output’ system is given. Concrete fabrication parameters of the lattice height, the substrate and coating layers are found. The coupling efficiency of radiation input into the waveguide is found to be 30%.

A 6f-scheme of Fourier holography with resonant architecture is considered, which implements memory replenishment with new information that contradicts the previously recorded. It is shown that the low-frequency attenuation due to the nonlinearity of the exposure characteristics of holographic recording media in the initial reference holographic image recorded in a narrow filtering range corresponding to the degradation in the correlation plane of the global maximum of the autocorrelation function below the lateral maxima leads to the response instability – an intermittent mode. It is shown that the intermittent mode corresponds to the restructuring of the autocorrelation function of a composite standard recorded in holograms from one range of values of the approximation model parameters to another. It is shown that the correlation length of the composite image recorded in holograms is an order parameter of the system; its rapid change precedes the loss of response stability and the transition to an unstable regime with intermittency. The results of numerical simulation are presented.

In this work, a hybrid-forecasting model is proposed. The model includes a recursive penalty P-spline with parameters adaptation based on evolutionary optimization algorithms. In short-term forecasting, especially in real-time systems, the urgent task is to increase the forecast speed without compromising its quality. High forecasting speed has been achieved by an economical computational scheme of a recurrent P-spline with a shallow depth of prehistory. When combined with the adaptation of some parameters of the P-spline, such an approach allows you to control the forecast accuracy.

When laser coagulation of eye retina is carried out, the laser beam is directed to target retinal areas selected by an ophthalmologist. The exposure to laser light produces a photocoagulate. When using laser coagulation, the main problem is selecting both the laser exposure areas that would cover all pathological zones and the laser exposure parameters to prevent retina damage. Any patient has an individual fundus structure. The individual structure of pathological and anatomical elements must be taken into account to achieve the desired therapeutic effect. The formation of coagulates in all hemorrhage-affected areas results in the desired therapeutic effect. The vascular layer must be heated to a sufficient temperature to form a coagulate. Such an effect can be predicted using mathematical modeling of laser exposure. In this paper, we consider methods of mathematical modeling of laser exposure based on the solution of a heat equation. The methods are compared in terms of their computational complexity and solution stability. An analysis of the possibility of predicting the therapeutic effect using methods of mathematical modeling of laser exposure is carried out.

The paper discusses different approaches to image and video processing aiming to solve the problems of detecting, tracking and estimating the parameters of moving objects. The developed algorithms for solving these problems are described in relation to the field of transport analytics. When developing the algorithms, attention was given to solving the problems on an embedded platform of video surveillance cameras, which imposes restrictions on the computational complexity. The first (basic) algorithm for moving object detection and parameter estimation is based on processing two associated areas of an image. This algorithm includes a computationally efficient adaptive procedure for evaluating and updating the background component of an image. The procedure is based on the physics of the process of movement of the object of interest through a processing zone. The second algorithm performs object tracking based on an optical flow method initialized by feature points. The third algorithm is based on object segment tracking and is computationally efficient for the implementation on an embedded platform of intelligent cameras. Results of experimental studies of the proposed algorithms are presented, as well as a comparison with some well-known algorithms. It is shown that tracking algorithms can improve the accuracy of moving object parameter estimation. Tracking also reduces the number of classification errors compared to the basic approach to object detection and parameter estimation.

In this paper we simulated the focusing of left circular polarized beam with a second order phase vortex and a second-order cylindrical vector beam by a gradient index Mikaelian lens. It was shown numerically, that there is an area with a negative Poynting vector projection on Z axis, that can be called an area with backward energy flow. Using a cylindrical hole in the output surface of the lens and optimizing it one can obtain a negative flow, which will be situated in the maximum intensity region, unlike to previous papers, in which such backward energy flow regions were situated in a shadow area. Thereby, this lens will work as an “optical magnet”, it will attract Rayleigh particles (with diameter about 1/20 of the wavelength) to its surface.

Using electronic beam lithography and reactive ion beam etching, a metalens is manufactured in a thin layer of amorphous silicon of a 130-nm depth, a 30-µm diameter, and a 633-nm focal length (equal to the illumination wavelength). The metalens is composed of 16 sectored subwavelength binary gratings with a 220-nm period. The uniqueness of this metalens is that when illuminated by left-handed circularly polarized light, it is capable of generating a left-handed circularly polarized vortex beam with a topological charge of 2, generating a second-order cylindrical vector beam when illuminated by linearly polarized light. Both for linear and circular incident polarization, an energy backflow is found to be generated in the vicinity of the tight focus. Transverse intensity distributions measured with a scanning near-field optical microscope near the focus of the metalens are in qualitative agreement with the intensity distributions calculated by the FDTD method. This confirms that a backward energy flow takes place at the focus of the metalens. A metalens generating an energy backflow near its focus is fabricated and characterized for the first time.

The article describes special aspects of the organization of nonlinear filtering of image contours defined as a sequence of complex numbers. An effect of scale and rotation of the contour on the filtering result is studied. Changes in the contour spectra caused by different nonlinear filters are studied.

In the development of modern logistics, the role of automated cargo warehousing is gradually reflected, which is essential for the automatic distribution of goods. This paper briefly introduced the automatic location allocation model and the particle swarm optimization (PSO) algorithm used to optimize the model. At the same time, it introduced the concept of genetic operator and multi-group co-evolution to improve the algorithm, and then the simulation analysis of standard PSO and improved PSO was performed on MATLAB software. The results showed that the improved PSO iterated fewer times and get better solution sets; compared with the manual allocation scheme, the improved PSO calculation reduced more warehousing time, lowered more center of gravity height, and improved shelf stability. In summary, the improved PSO algorithm can effectively optimize the automated goods dynamic allocation and warehousing model.

The feasibility of generating surface plasmon polaritons carrying orbital angular momentum ("vortex plasmons") on cylindrical conductors by an end-fire coupling technique in the spectral range from 8.5 to 141 μm (~ 2-40 THz) is considered. The front face of the cylinder is illuminated by Bessel beams formed using binary spiral phase axicons, or annual vortex beams formed in the focal plane of an additional lens. Graphs are constructed that reveal the relationship between the waveguide parameters (conductor diameter, which is equal to the diameter of the illuminating beam, and the “twist” angle of the plasmon) and the axicon parameters (the ratio of the axicon period to the radiation wavelength) for the above wavelengths and topological charges of the beams ranging from 1 to 9. The results obtained indicate the possibility of conducting experiments in the long-wavelength range for modeling a plasmon multiplex communication channel.

The design of a freeform mirror generating a uniform illuminance distribution in a rectangular region with angular dimensions of 30°x15° is presented. The design method is based on the formulation of the problem of calculating the "ray-mapping" as a Monge-Kantorovich mass transportation problem and its subsequent reducing to a linear assignment problem. We describe a mirror fabrication process with the use of milling technology and present results of experimental measurements of the light distribution generated by the mirror. The experimental results are in good agreement with the results of numerical simulations and thus confirm the manufacturability of mirrors designed by the method proposed.

This paper considers methods for simulating color underwater images based on real terrestrial images. Underwater image simulation is widely used for developing and testing methods for improving underwater images. A large group of existing methods uses the same deterministic image transformation model ignoring the presence of noise in images. The paper demonstrates that this significantly affects the overall quality of underwater images simulation. It is shown both theoretically and numerically that the accuracy of the signal-to-noise ratio of underwater images simulated using a deterministic transformation decreases with increasing distance to the object. To solve this problem, a new model of image transformation for simulating underwater images based on terrestrial images is proposed, which considers the presence of noise in the image and is compatible with all simulating methods from the group under consideration. The paper presents the results of the simulation based on the existing and proposed models, showing that at long distances, the new results are better consistent with real data.

In order to resist network malicious attacks, this paper briefly introduced the network intrusion detection model and K-means clustering analysis algorithm, improved them, and made a simulation analysis on two clustering analysis algorithms on MATLAB software. The results showed that the improved K-means algorithm could achieve central convergence faster in training, and the mean square deviation of clustering center was smaller than the traditional one in convergence. In the detection of normal and abnormal data, the improved K-means algorithm had higher accuracy and lower false alarm rate and missing report rate. In summary, the improved K-means algorithm can be applied to network intrusion detection.

The paper considers a problem of extracting user preferences based on their photo gallery. We propose a novel approach based on image captioning, i.e., automatic generation of textual descriptions of photos, and their classification. Known image captioning methods based on convolutional and recurrent (Long short-term memory) neural networks are analyzed. We train several models that combine the visual features of a photograph and the outputs of an Long short-term memory block by using Google's Conceptual Captions dataset. We examine application of natural language processing algorithms to transform obtained textual annotations into user preferences. Experimental studies are carried out using Microsoft COCO Captions, Flickr8k and a specially collected dataset reflecting the user’s interests. It is demonstrated that the best quality of preference prediction is achieved using keyword search methods and text summarization from Watson API, which are 8 % more accurate compared to traditional latent Dirichlet allocation. Moreover, descriptions generated by trained neural models are classified 1 – 7 % more accurately when compared to known image captioning models.

Technological features of the growth of single crystal silicon carbide inevitably create condi-tions for the formation of crystal structure defects in them. A method is proposed for recognizing and analyzing a dislocation structure of single crystal silicon carbide based on the use of optical microscopy and a direct distribution neural network. The method was tested on homoepitaxial lay-ers of 4H-polytype silicon carbide. Software has been developed that allows building maps of the dislocation structure distribution over the surface of single crystal silicon carbide. The software was tested on digital images of the surface of silicon carbide epitaxial layers. The accuracy of recognition of dislocation structure was 95%. The dislocation mapping is used in the development of process technologies for reducing their density during the growth of single crystals.

We developed and implemented an intensity moments technique for measuring amplitude and initial phase spectra, the topological charge (TC) and orbital angular momentum (OAM) of the Laguerre-Gauss (LG) beams decomposed into the basis of Hermite-Gaussian (HG) modes. A rigorous theoretical justification is given for measuring the TC of unperturbed LG beams with different values of radial and azimuthal numbers by means of an astigmatic transformation on a cylindrical lens. We have shown that the measured amplitude and phase spectra of the HG modes make it possible to find the orbital OAM and TC, as well as digitally sorting the HG modes and then restoring the initial singular beam.

This paper presents the results of an analysis of the necessary propagation conditions in a step-index optical fiber with a Kerr nonlinearity of two modes, LP01 and LP11, during the transmission of high-power optical pulses. All results were obtained by solving a system of two nonlinear equations for these modes, obtained by the Gauss approximation method, and the subsequent use of a procedure for refining estimates using the mixed finite elements method. The necessary conditions are determined, estimates of the boundaries for the range of normalised frequencies for which they are fulfilled are obtained, and an approximate formula is proposed for estimating the upper limit of this range.

On the basis of diffraction transformations of an optical wave field a mathematical model for the formation of speckle modulated interference patterns and signals at the output of a speckle interferometer is developed, which allows us to identify their properties and quantitative parameters. Speckle interferometers based on a Michelson arrangement are considered, where objects with scattering surfaces are used instead of mirrors in the reference and object arms. Results of numerical simulation of speckle modulated interference patterns on the basis of diffraction transformations of wave fields in an interferometer are discussed. Simulated images obtained at the output of the interferometer when focusing laser beams on the scattering surfaces of the controlled and reference objects are considered. Experimental results of using a speckle interferometer with a digital matrix photodetector for measuring the temperature micro-displacements of an object with a scattering surface and a quantitative comparison of experimental data with the results obtained by a numerical experiment using a diffraction model of a speckle interferometer are presented.

Here we theoretically obtain values of the topological charge (TC) for vortex laser beams devoid of radial symmetry: asymmetric Laguerre-Gaussian (LG) beams, Bessel-Gaussian (BG) beams, Kummer beams, and vortex Hermite-Gaussian (HG) beams. All these beams consist of conventional modes, namely, LG, BG, or HG modes, respectively. However, all these modes have the same TC equal to that of a single constituent mode n. Orbital angular momenta (OAM) of all these beams, normalized to the beam power, are different and changing differently with varying beam asymmetry. However, for arbitrary beam asymmetry, TC remains unchanged and equals n. Superposition of just two HG modes with the adjacent numbers (n, n+1) and with the phase retardation of (pi)/2 yields a modal beam with the TC equal to – (2n+1). Numerical simulation confirms the theoretical predictions.

In contrast to the orbital angular momentum (OAM), which is conserved on free space propagation, the topological charge (TC) of a paraxial optical vortex (OV) is not conserved in the general case. Here, we investigate a Gaussian beam with a fractional TC in the original plane and demonstrate both theoretically and numerically how the TC changes in the course of propagation. Depending on the proximity of the topological charge to an even or odd integer number, an optical vortex with the original fractional TC is shown to behave in a number of different ways. For simple OVs (Laguerre-Gaussian or Bessel-Gaussian modes), TC is conserved both in propagation and after weak phase distortions. An experiment shows that when scattered by a random phase screen, the integer TC of an OV is conserved right up to a random phase variation of π. Therefore, in the case of weak turbulences, it is expedient to measure a discretely varying TC instead of a continuously varying OAM.

A method for the synthesis of holograms that reconstruct a 3D image using two 2D images, including a topographic map of the surface of a 3D object and photographs of the texture of its surface obtained using coherent and incoherent radiation is proposed. The method is suitable for transmitting information about a 3D object via a communication channel at a distance and subsequent synthesis of holograms at a distance from the holography object. It can be used in augmented reality devices, when creating holographic cameras and 3D video cameras, in 3D projectors and microscopes, devices with a synthesized aperture. The proposed method for the remote hologram synthesis is suitable for reproducing 3D holographic images of distant objects at different parts of the electromagnetic spectrum. Examples of 3D image restoration in the infrared spectral range are given. The method is also suitable for the synthesis of holograms in the UV, THz, and even radio range.

The paper is devoted to the development of an effective semantic segmentation algorithm for automation of airport infrastructure labelling in RGB satellite images. This task is addressed using algorithms based on deep convolutional artificial neural networks, as they have proven themselves in a wide range of tasks, including the terrestrial imagery segmentation, where they show consistently high results. A new dataset was labelled for this particular task and a comparative analysis of different architectures and backbones was carried out. A conditional random field model (CRF) was used for postprocessing and accounting of contextual information and neighborhood of objects of different classes in order to eliminate outliers. Features of the solutions applied at all preparatory stages of the algorithm were described, including data preparation, neural network training and post-processing of the training results.

The article proposes a modification of the Blurred Image Matching (BIM) method, a key point selection method in images, thus solving a problem of their accurate comparison. A new approach for blobs selection and comparison is proposed. The use of these modifications allows us to achieve an increase in the proportion of correctly matched pairs of images by 30.2% compared to the basic method when working with noisy data.

When solving a variety of celestial navigation tasks there is a problem of determining parameters of spacecraft motion and onboard primary payload orientation based on the coordinates of registered star images. Furthermore, unwanted objects, like active satellites, natural and artificial space debris, that reduce the probability of correct recognition may get into the field of view of a satellite sensor. This prompts the necessity to filter out such interference from the star field images. However, if the objects under recognition are bodies located in near-Earth space, in this case, the star images themselves will act as interferences. In addition, since the detection and cataloging of these objects from the Earth’s surface is complicated by their small size, the atmospheric effects, as well as other technical difficulties, it is worthwhile to use the existing equipment onboard spacecrafts to solve this task. The existing recognition algorithms for star groups, as well as their classification, are presented in this paper. Moreover, a structurally topological approach for identifying groups of stars based on the properties of enveloping polygons used in constructing topological star patterns is proposed. Specific features in the construction of topological configurations on the analyzed set of points, as well as the principles of dynamic space object detection within their limits are described. Results of the numerical experiments performed using the developed algorithm on the star field maps and model scenes are presented.

The effect of diffraction on the directional light transmittance of grating optical filters designed for smart windows is studied. The filter has an angular selective light transmission due to two thin-film gratings formed by "non-transmissive" (absorptive, reflective or scattering) parallel strips on the surfaces of a single or double glazed window. Methods are considered for calculating the op-timal slope angle of two filter’s gratings, their relative position on the opposite window surfaces and the widths of the strips of both gratings to minimize light transmission on a specific date and time of day for a given latitude and longitude of the building and azimuth of the window. A meth-od has been developed for calculating the diffraction at the input and output gratings of the filter with regard for a complex movement of the Sun relative to the window and a corresponding change in the incidence angle of the solar beams on the gratings. To evaluate a decrease in the light transmittance of the filter due to diffraction, an additional factor is introduced into the calcu-lation equation. Geometric parameters of the filters and diffraction are calculated. The values of the diffraction factor are 0.9999992 and 0.9999998 for single or double glazed windows, respec-tively, i.e., the influence of diffraction on the light transmittance of the grating filter can be ne-glected.

The article discusses techniques for the precise positioning of a probe tip on the surface under study in scanning probe microscopy. The precise tip positioning is of high importance when studying the same sample with various microscopes, probes, in various environments and conditions. It is offered that the precise positioning should be done by applying on the surface equidistant reference marks with a nanoindentor along a straight line. The article describes a procedure for shifting the microscope’s field of view for detecting the reference marks and subsequently moving the probe along the marks line. A reference marks recognition algorithm is presented. The algorithm is based on the image correlation analysis using a master mark image and the subsequent extraction of special points (at local maxima of the correlation coefficient). A descriptor of mutual distances between the special points and the Pearson coefficient (аs a quantitative criterion of recognition) are used for recognition. Three types of reference marks are considered: a circle, a circle with gradient filling, and a circle with continuous filling. It is shown that the number of the allocated special points can be significantly larger than that of the reference marks. A method for filtering special points is offered. The method is based on the threshold filtering of special points using values of the correlation coefficient. The filtering consists in excluding those special points in whose neighborhood the difference of the maximum and minimum of the correlation coefficient does not exceed a preset threshold. Results confirming the effectiveness of the offered filtering method are presented. Recommendations for choosing the parameters of the recognition and filtering algorithms are given.

In this paper, we propose an incremental learning scheme for the abnormal behavior detection algorithm based on principal component. The results obtained on a UCSD dataset and our experimental videos at a different number of training samples show that error rates are similar to conventional learning. Moreover, the proposed scheme allows the incremental learning time to be significantly reduced in comparison with a method based on matrix eigendecomposition.

In this paper a new method for contour detection in grayscale images is proposed. The pro-posed method is based on the use of an image weight model, which allows one to estimate its pix-els from the point of view of their significance for perception. In this case, the most significant pixels are those that contain characteristic features of the image, including brightness differences at the boundaries of the regions. To assess the significance of pixels, we propose a procedure for analyzing the contribution of the corresponding wavelet coefficients at different scale levels to the total energy of the image. The described method of contour detection involves building an image weight model, determining the directions of linear segments along the edges on the weight image, analyzing the significance of pixels and linking significant pixels. The advantage of the method is the high operation speed (the corresponding loop detector works on average four times faster than the Canny edge detector). In addition, the paper describes a detector of significant image areas, which is also based on the weight model. The proposed approach can be used in various systems of information processing and control based on methods and tools of computer vision, including control and navigation systems of unmanned vehicles, remote sensing of the Earth, systems for pavement defect detection, biometric systems, etc.

In this paper questions of building models of information exchange systems with discrete and distributed delay and with delayed feedback by methods of the theory of linear functional differential equations are investigated. When solving the said equations, it is necessary to consider restrictions caused by the uncertainties in the system under modeling, such as the absence of the exact data on the parameters of the model elements, their natural spread and temporal variations, thus requiring the solution of an identification problem. The models with continuous aftereffect introduced in this work take a fuller account of reflected signal characteristics in closed space, which increases the reliability of modeling results in comparison with the known differential models. At the same time, there is a problem of finding functions that characterize the value distribution of the echo delay. In this work, these functions (kernels) are approximated by a number of exponents, which simplifies the equations and allows the assumption that the aftereffect can be concentrated on both final and infinite intervals. The echo components caused by closed-space resonances are modeled by transfer functions of the corresponding linear links. In numerical modeling, a single-channel model represented by a second-order resonance link and a pulse-shaped kernel described by a sum of two decreasing exponents is considered. The analysis of stability of the models of systems with delayed feedback is conducted by a frequency method. In the paper an approach to estimating the correlation and spectral functions of signals and noise components based on the parametric representation of the latter is considered. The paper also considers issues relating to the practical significance of the research results.

The paper discusses a method of visual attention based on vertex ranking of graphs on the basis of image features. The aim of the research is to develop a method that allows high-precision detection of objects in images with low color contrast between the selected and background areas. The image is pre-segmented into regions to calculate the saliency map. The graph is based on regions. Each region is associated with related regions, as well as with areas adjacent to adjacent regions. The regions are vertices of the graph. The vertices of the graph are ranked according to the characteristics of the corresponding image areas. The scope is highlighted based on requests from background areas. The saliency map is determined based on background area queries. Regions adjacent to the edges of the image belong to the background areas. Color features of the image were used in the existing approach of visual attention based on the manifold ranking. Texture features and shape features are additionally used in the proposed method to improve accuracy. Gabor's energy function is used to calculate texture features. The distance between centers of the regions is calculated by analyzing the form. The proposed method has shown good results for detecting objects in images in which the background color and object color are in similar ranges. The experimental results are presented on test images. Precision-recall curves showing the advantage of the developed method are constructed.

In this work, we propose a high-resolution optical system for Earth remote sensing, operating at 200-3300-nm wavelengths and providing a 4º field of view. Parameters of the system's structural elements are calculated and presented. Dot charts of the spots of confusion for the center, intermediate zones, and the edge of the field are considered. Over most of the operating wavelengths, the optical system is shown to be diffraction-limited, which provides attaining the highest possible spatial resolution. This system is considered as a tool for monitoring of the Earth's surface and collection of information in the ultra-violet, visible and near infrared spectral ranges (200-3300 nanometers).

We compare approaches for the automatic detection of pathological changes in brain MRI images that are visible to the naked eye. We analyse multi-stage approaches based on deep learning and threshold processing. A convolutional neural network was formed, a classifier was built based on the use of an ensemble of decision trees, and an algorithm was created for multi-stage image processing. Because of experimental studies, it was found that the most effective method for recognizing images of magnetic resonance imaging is an approach based on an ensemble of decision trees. With its help, 95 % of the images from the test sample were classified correctly. At the same time, using the convolutional neural network, it was possible to classify correctly all images containing the area of pathological changes. The data obtained can be used in practice for the diagnosis of brain diseases, for automating the processing of a large number of studies of magnetic resonance imaging.

The paper studies the possibility of using neural networks for the classification of objects that are few or absent at all in the training set. The task is illustrated by the example of classification of rare traffic signs. We consider neural networks trained using a contrastive loss function and its modifications, also we use different methods for generating synthetic samples for classification problems. As a basic method, the indexing of classes using neural network features is used. A comparison is made of classifiers trained with three different types of synthetic samples and their mixtures with real data. We propose a method of classification of rare traffic signs using a neural network discriminator of rare and frequent signs. The experimental evaluation shows that the proposed method allows rare traffic signs to be classified without significant loss of frequent sign classification quality.

An optical vortex passed through an arbitrary aperture (with the vortex center found within the aperture) or shifted from the optical axis of an arbitrary axisymmetric carrier beam is shown to conserve the integer topological charge (TC). If the beam contains a finite number of off-axis optical vortices with different TCs of the same sign, the resulting TC of the beam is shown to be equal to the sum of all constituent TCs. For a coaxial superposition of a finite number of the Laguerre-Gaussian modes (n, 0), the resulting TC equals that of the mode with the highest TC (including sign). If the highest positive and negative TCs of the constituent modes are equal in magnitude, then TC of the superposition is equal to that of the mode with the larger (in absolute value) weight coefficient. If both weight coefficients are the same, the resulting TC equals zero. For a coaxial superposition of two different-amplitude Gaussian vortices, the resulting TC equals that of the constituent vortex with the larger absolute value of the weight coefficient amplitude, irrespective of the relation between the individual TCs.

A method of image fusion based on wavelet decomposition of the original images is considered. An integrated monochrome image is formed from images of the same scene obtained in different spectral ranges. A strategy for fusing detail coefficients by comparing the proportions of their magnitudes for all original images is proposed. The fusion procedure does not require any thresholds. This fusion procedure can be performed for any number of original images. The algorithm does not introduce additional distortions and collects all the necessary information from the original images. The quantitative and qualitative evaluation of the results was performed. The proposed algorithm can be used in optoelectronic systems for automatic image processing.