Computer Optics

5-12

Authors: V.V. Kotlyar, A.G. Nalimov

In a number of theoretical (J. Opt. 6, 259 (2004)) and experimental (Opt Express 19, 5760 (2011)) works, an original fractional-TC optical vortex (with TC standing for topological charge) was shown to evolve into an integer optical vortex whose TC is equal to the nearest (i) smaller integer, if the fractional part is smaller than 1/2, and (ii) larger we show that the initial fractional optical vortex evolves into an integer optical vortex with TC equal to the nearest (i) smaller integer, if the fractional part is smaller than 0.12, and (ii) larger integer, if the fractional part is larger than 0.12. This can be explained by the fact that the additional center integer, if the fractional part is larger than 1/2. In this work, using numerical simulation, of singularity is generated on the beam periphery characterized by near zero-intensity (a millionth of the maximum), thus prohibiting the experimental detection, but allowing a numerical assessment.

19-28

Authors: V.V. Kotlyar, A.A. Kovalev

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.

38-44

Authors: A.V. Dyshlyuk, U.A. Eryusheva, O.B. Vitirk

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.

48-57

Authors: M.V. Davidovich

We consider dissipative Dyakonov plasmon-polaritons as surface waves propagating along the plane boundary of a hyperbolic metamaterial with an arbitrary orientation of the crystallographic axis. Conditions for the existence of fast, slow, gliding flowing, forward and backward plasmon-polaritons are found. A waveguide in the form of an asymmetric layer of a hyperbolic metamaterial is also considered. An expression for the density of electromagnetic energy in such a metamaterial is given.

66-76

Authors: E.I. Ershov, S.A. Korchagin, V.V. Kokhan, P.V. Bezmaternykh

The classical Otsu method is a common tool in document image binarization. Often, two classes, text and background, are imbalanced, which means that the assumption of the classical Otsu method is not met. In this work, we considered the imbalanced pixel classes of background and text: weights of two classes are different, but variances are the same. We experimentally demonstrated that the employment of a criterion that takes into account the imbalance of the classes' weights, allows attaining higher binarization accuracy. We described the generalization of the criteria for a two-parametric model, for which an algorithm for the optimal linear separation search via fast linear clustering was proposed. We also demonstrated that the two-parametric model with the proposed separation allows increasing the image binarization accuracy for the documents with a complex background or spots.

90-100

Authors: I.A. Konovalenko, V.V. Kokhan, D.P. Nikolaev

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.

110-121

Authors: M.V. Chukalina, A.V. Khafizov, V.V. Kokhan, A.V. Buzmakov, R.A. Senin, V.I. Uvarov, M.V. Grigoriev

An algorithm for post-processing of the grayscale 3D computed tomography (CT) images of porous structures with the automatic selection of filtering parameters is proposed. The determination of parameters is carried out on a representative part of the image under analysis. A criterion for the search for optimal filtering parameters based on the count of "levitating stone" voxels is described. The stages of CT image filtering and its binarization are performed sequentially. Bilateral and anisotropic diffuse filtering is implemented; the Otsu method for unbalanced classes is chosen for binarization. Verification of the proposed algorithm was carried out on model data. To create model porous structures, we used our image generator, which implements the function of anisotropic porous structures generation. Results of the post-processing of real CT images containing noise and reconstruction artifacts by the proposed method are discussed.

130-141

Authors: V. Evdokimova, M. Petrov, M. Klyueva, E. Zybin, V. Kosianchuk, I. Mishchenko, V. Novikov, N. Selvesiuk, E. Ershov, N. Ivliev, R. Skidanov, N. Kazanskiy, A. Nikonorov

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.

149-153

Authors: V.G. Efremtsev, N.G. Efremtsev, E.P. Teterin, P.E. Teterin, E.S. Bazavluk

The use of neural networks to detect differences in radiographic images of patients with pneu-monia and COVID-19 is demonstrated. For the optimal selection of resize and neural network ar-chitecture parameters, hyperparameters, and adaptive image brightness adjustment, precision, recall, 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 express-diagnostic methods.