Computer Optics

691-701

Authors: D.V. Soshnikov, L.L. Doskolovich, E.V. Byzov

We consider a gradient method for calculating cascaded diffractive optical elements (DOEs) consisting of several sequentially placed phase DOEs. Using the unitarity property of the operator describing the light propagation through the cascaded DOE, we obtained explicit expressions for the derivatives of the error functional with the respect to the phase functions of the cascaded DOE. We consider the application of the gradient method in the problem of focusing several different incident beams to several domains with different intensity distributions, and in the problem of image classification. The presented description of the gradient method treats the problems of designing cascaded DOEs for both focusing the laser radiation and performing image classification in the framework of a single general approach. It is shown that the difference of the problem of optical classification from the problem of generating required intensity distributions consists only in the form of error functionals, the calculation of the derivatives of which is reduced to the same general formula. Using the proposed gradient method, we designed single and cascaded DOEs for optical classification of handwritten digits. The obtained results may find application in the development of diffractive neural networks and optical systems for laser beam focusing.

710-715

Authors: V.V. Kotlyar, S.S. Stafeev, E.S. Kozlova

In this work, by the Richards-Wolf method, which describes the behavior of electromagnetic radiation at the tight focus, it is shown that high-order spin and orbital Hall effects take place in the focal plane. It is shown that when focusing a linearly polarized optical vortex with unit topological charge, four local subwavelength regions are formed in the focal plane, in which directions of the longitudinal projection of the spin angular momentum are opposite in the neighboring regions. That is, photons falling into neighboring regions in the focus have the opposite spin. This is the spin Hall effect of the 2nd order. It is also shown that when tightly focusing of superposition of cylindrical vector beams of the m-th order and zero order, 2m subwavelength regions are formed in the plane of tight focus, in which directions of the longitudinal projection of the orbital angular momentum are opposite in the neighboring regions. That is, photons falling into the neighboring regions at the focus have the opposite-sign on-axis projections of the orbital angular momentum. This is the orbital Hall effect of the m-th order.

725-733

Authors: S.I. Kharitonov, V.A. Fursov

We offer a computer technology for modeling a process of optical imaging with a diffractive imaging lens. The central idea of the technology is to evaluate the quality of the optical system by matching the input and output images against criteria adopted in image processing. For this purpose, same-resolution hyperspectral images are fed to the input and generated at the output. Thanks to the large number of spectral components, a fairly accurate reproduction of the effects associated with the dependence of the refractive index on the wavelength is ensured. To compare input and output images in terms of PSNR (peak signal-to-noise ratio), standard three-component RGB images are "assembled" using standard matching functions over the entire optical range. Results of the study of the dependence of the PSNR indicator on the main parameters of the optical system are given: focal length, linear aperture and the number of diffraction orders taken into account.

742-750

Authors: P.A. Khorin, A.M. Algubili, S.A. Degtyarev, S.K. Sergunin, S.V. Karpeev, S.N. Khonina

We investigate polarization transformations carried out with a refractive bi-conical axicon using the FDTD method. The approach is based on the transformation of a circularly polarized optical beam into an azimuthally polarized beam due to the use of a single refractive element with two conical surfaces. On the inner surface of the element, polarization conversion occurs due to the reflection and refraction of rays at the Brewster angle, while the outer surface operates as a converted beam collimator. The distributions of the components of the electric field vector and the polarization vector at different distances from the optical element are considered as criteria for a successful polarization transformation. By numerical simulation of the performance of a bi-conical axicon made of glass with a refractive index of n = 1.4958, the efficiency of the proposed approach for a circularly polarized Gaussian beam with a wavelength of λ = 1.5 µm is shown. The proposed element is shown to be immune to chromatic aberrations in a significant range of changes in the refractive index of the element material and incident wavelengths (1.5 ≤ n ≤ 1.7; 1 μm ≤ λ ≤ 1.5 μm).

761-769

Authors: V.Y. Kolyuchkin, N.M. Kostylev, Y.S. Gulina

The article describes a methodology for performance evaluation of vision systems for remotely operated underwater vehicles. The methodology is based on a system approach and uses mathematical models of the aqueous medium where an optical signal propagates, the underwater object image registration system, and the mathematical model of the human visual system. The detection and recognition probabilities of underwater object image at a given registration range are used as performance evaluation indicators of underwater vision systems. The mathematical model of the aqueous medium developed by the authors allows quantitative evaluation of the influence of backscattering interference arising during objects illumination on the underwater vision system performance. The results of numerical experiments presented in the paper illustrate the possibility of using the proposed technique to optimize the underwater object image registration system parameters in order to achieve the required values of detection or recognition probabilities at the given ranges.

778-787

Authors: N.A. Sokolov, E.P. Vasiliev, A.A. Getmanskaya

Abstract: Advanced microscopy technologies such as electron microscopy have opened up a new field of vision for biomedical researchers. The use of artificial intelligence methods for processing EM data is largely difficult due to the small amount of annotated data at the training stage. Therefore, we add synthetic images to an annotated real EM dataset or use a fully synthetic training dataset. In this work, we present an algorithm for the synthesis of 6 types of organelles. Based on the EPFL dataset, a training set of 1161 real fragments 256×256 (ORG) and 2000 synthetic ones (SYN), as well as their combination (MIX), were generated. The experiment of training models for 6, 5-classes and binary segmentation showed that, despite the imperfections of synthetics, training on a mixed (MIX) dataset gave a significant increase (about 0.1) in the Dice metric for 6 and 5 and same results at binary. The synthetic data strategy gives annotations for free, but shifts the effort to producing sufficiently realistic images.

795-805

Authors: N.A. Firsov, V.V. Podlipnov, N.A. Ivliev, D.D. Ryskova, A.V. Pirogov, A.A. Muzyka, A.R. Makarov, V.E. Lobanov, V.I. Platonov, A.N. Babichev, V.A. Monastyrskiy, V.I. Olgarenko, D.P. Nikolaev, R.V. Skidanov, A.V. Nikonorov, N.L. Kazanskiy, V.A. Soyfer

The paper presents a study of various approaches to the classification of soil covers based on neural network algorithms using hyperspectral remote and proximal sensing of the Earth. The spectral distributions were recorded in the laboratory using an Offner imaging scanning hyperspectrometer. Spectral-spatial characteristics of nine soil samples from various parts of a farming land in the Samara region were experimentally studied. Using a method of energy dispersion microanalysis, the correspondence between the hyperspectral data and the chemical composition of the samples taken was established. Based on the data obtained, a neural network-aided classification of soil samples was implemented depending on the content of constituent elements such as carbon and calcium. A normalized spectral-spatial convolutional neural network was used as a classifier. As a result of the work, an approach to the classification of high-resolution hyper-spectral images based on the refinement of a multiclass convolutional neural network using an ensemble of binary classifiers is proposed. It is shown that the classification of soil samples by carbon and calcium content is carried out with an accuracy of 0.96.

816-823

Authors: A.N. Galyntich, M.A. Raifeld

When there is no information about the number of brightness classes, synthesizing algorithms for automatic image threshold segmentation involves a problem of determining the number of thresholds. The solution to the problem of estimating the number of classes in an image can be based on representing its distribution as a mixture of distributions of brightness classes when priori probabilities are unknown, or estimating the number of histogram modes. At the same time, it is known that the mixture splitting problem has a solution only for certain types of distributions and the histogram modes are not always distinguishable. In the general case, when the distributions of brightness classes are unknown, there are difficulties in applying these methods. The article proposes a non-parametric approach to determining the number of classes that differ in average brightness, based on rank histograms and using the property of local spatial grouping of elements of each brightness class in the image.

832-840

Authors: L.A. Manilo, A.P. Nemirko

More and more attention is being paid to the development of methods for the objective analysis of biosignals for computer medical systems. The search for new non-standard methods is aimed at improving the reliability of diagnostics and expanding the areas of their practical application. In this paper, methods for recognizing biomedical signals by the degree of severity of their nonlinear components are considered. An approach based on the use of approximate entropy closely related to Kolmogorov entropy (K-entropy) is used. Its parameters can be used to detect dynamic irregularities associated with nonlinear properties of signals. The algorithm for calculating this characteristic is consid-ered in detail. Based on model experiments, its main properties are analyzed. It is shown that the entropy of a finite sequence, calculated in accordance with a multistep pro-cedure, can give an erroneous estimate of the degree of regularity of the signal. A procedure for correcting the approximate entropy is proposed, which expands the area of analysis of this function for estimating nonlinearity. It has been established that the transition to adjusted entropy makes it possible to increase the reliability of the detection of chaotic components. A set of entropy parameters is proposed for constructing recognition procedures. Examples of solving the problems of detecting atrial fibrillation by the parameters of the non-linearity of the rhythmogram, as well as assessing the depth of anesthesia by the electroencephalogram (EEG) are given. Experiments conducted on real recordings of electrocardiogram (ECG) and EEG signals have shown the high efficiency of the proposed algorithms. The proposed methods and algorithms can be used in the development of systems for monitoring ECG of cardiological patients, as well as monitoring the depth of anesthesia by EEG during surgical operations.