Methods for image noise level estimation

The article presents a method for estimating the level of discrete white noise in an image, based on the use of linear difference operators with a vector mask. Two variants of a new method for estimating the noise level are proposed, which differ in the accuracy of the obtained estimates and computational complexity. The first version of the method can be attributed to the class of block methods, whereas the second one is intended for the rapid image analysis and is based on processing a small number of rows or columns of an image.

Comparison of numerical integration methods for calculating diffraction of a plane electromagnetic wave by a rectangular aperture

We discuss features of the calculation of a Fraunhofer integral by traditional quadrature numerical integration methods and a special collocation Levin method when calculating the diffraction of a plane electromagnetic wave by a rectangular aperture. For the quadrature numerical integration methods, a criterion for the assessment of the integration step is derived depending on the screen size and required calculation accuracy. Advantages of the use of the special collocation Levin method in comparison with the traditional quadrature numerical integration methods are shown.

Sharp focusing of beams with V-point polarization singularities

It is theoretically and numerically shown that when tightly focusing an n-th order vector light field that has the central V-point (at which the linear polarization direction is undetermined), the polarization singularity index n, and a "flower"-shaped intensity pattern with 2(n-1) lobes it forms a transverse intensity distribution with 2(n-1) local maxima. At the same time, a vector light field with the polarization singularity index -n, which has the form of a "web" with 2(n+1) cells generates at the sharp focus a transverse intensity distribution with 2(n+1) local maxima. In the focal spot, either 2(n-1) or 2(n+1) V-point polarization singularities with alternating indices +1 or -1 are formed at the intensity zero.

Nonparametric pattern recognition algorithm for testing a hypothesis of the independence of random variables

A new method for testing a hypothesis of the independence of multidimensional random variables is proposed. The technique under consideration is based on the use of a nonparametric pattern recognition algorithm that meets a maximum likelihood criterion. In contrast to the traditional formulation of the pattern recognition problem, there is no a priori training sample. The initial information is represented by statistical data, which are made up of the values of a multivariate random variable. The distribution laws of random variables in the classes are estimated according to the initial statistical data for the conditions of their dependence and independence. When selecting optimal bandwidths for nonparametric kernel-type probability density estimates, the minimum standard deviation is used as a criterion. Estimates of the probability of pattern recognition error in the classes are calculated. Based on the minimum value of the estimates of the probabilities of pattern recognition errors, a decision is made on the independence or dependence of the random variables. The technique developed is used in the spectral analysis of remote sensing data.

Automated combination of optical coherence tomography images and fundus images

We discuss approaches to combining multimodal multidimensional images, namely, three-dimensional optical coherence tomography (OCT) data and two-dimensional color images of the fundus. Registration of these two modalities can help to adjust the position of the obtained OCT images on the retina. Some existing approaches to matching fundus images are based on finding key points that are considered invariant to affine transformations and are common to the two images. However, errors in the identification of such points can lead to registration errors. There are also methods for iterative adjustment of conversion parameters, but they are based on some manual settings. In this paper, we propose a method based on a full or partial search of possible combinations of the OCT image transformation to find the best approximation of the true transformation. The best approximation is determined using a measure of comparison of preprocessed image pixels. Further, the obtained transformations are compared with the available true transformations to assess the quality of the algorithm. The structure of the work includes: pre-processing of OCT and fundus images with the extraction of blood vessels, random search or grid search over possible transformation parameters (shift, rotation and scaling), and evaluation of the quality of the algorithm.

Experimental investigation of the self-healing of terahertz Bessel beams with orbital angular momentum

Experimental results of the investigation of self-healing properties of terahertz Bessel beams with orbital angular momentum (OAM) with topological charges of l=3 and l=4 in free space after passing through a dispersive medium are presented.

Road images augmentation with synthetic traffic signs using neural networks

Traffic sign recognition is a well-researched problem in computer vision. However, the state of the art methods works only for frequent sign classes, which are well represented in training datasets. We consider the task of rare traffic sign detection and classification. We aim to solve that problem by using synthetic training data. Such training data is obtained by embedding synthetic images of signs in the real photos. We propose three methods for making synthetic signs consistent with a scene in appearance. These methods are based on modern generative adversarial network (GAN) architectures. Our proposed methods allow realistic embedding of rare traffic sign classes that are absent in the training set. We adapt a variational autoencoder for sampling plausible locations of new traffic signs in images. We demonstrate that using a mixture of our synthetic data with real data improves the accuracy of both classifier and detector.

Classification of plumage images for identifying bird species

This paper studies the possibility of using neural networks to classify plumage images in order to identify bird species. Taxonomic identification of bird plumage is widely used in aviation ornithology to analyze collisions with aircraft and develop methods for their prevention. This article provides a method for bird species identification based on a dataset made up in the previous research. A method for identifying birds from real-world images based on YoloV4 neural networks and DenseNet models is proposed. We present results of the feather classification task. We selected several deep learning architectures (DenseNet based) for a comparison of categorical crossentropy values on the provided dataset. The experimental evaluation has shown that the proposed method allows determining the bird species from a photo of an individual feather with an accuracy of up to 81.03 % for accurate classification, and with an accuracy of 97.09 % for the first five predictions.

Neural network classification system for pigmented skin neoplasms with preliminary hair removal in photographs

The article proposes a neural network classification system for pigmented skin neoplasms with a preliminary processing stage to remove hair from the images. The main difference of the proposed system is the use of the stage of preliminary image processing to identify the location of the hair and their further removal. This stage allows you to prepare dermatoscopic images for further analysis in order to carry out automated classification and diagnosis of pigmented skin lesions. Modeling was carried out using the MatLAB R2020b software package on clinical dermatoscopic images from the international open archive ISIC Melanoma Project. The proposed system made it possible to increase the recognition accuracy of pigmented skin lesion images in 10 diagnostically important categories up to 80.81%. The use of the proposed system for the recognition and classification of images of dermatoscopic pigmented lesions by specialists will make it possible to increase the diagnostic efficiency in comparison with methods of visual diagnosis, and will also allow starting treatment at an earlier stage of the disease, which directly affects the survival and recovery rates for patients.

Quality of radiation conversion under four-wave mixing on thermal nonlinearity with feedback

Quality of radiation conversion under four-wave mixing on thermal nonlinearity with feedback for both signal and object waves has been investigated at high reflection coefficients. It has been shown that the optimal operating mode of a four-wave converter on thermal nonlinearity is the mode in which the pumping waves have equal intensities and there is a compensation for a phase shift arising from the pumping wave self-action. In this operating mode of the four-wave radiation converter, as compared with the case of the absence of feedback for both signal and object waves, a significant increase in the amplitude reflection coefficient is observed with an increase in the pumping waves intensities. In this case, despite the decrease in the bandwidth of spatial frequencies of the object wave with an increase in the pumping wave intensities, the quality of radiation conver-sion with feedback for both signal and object waves is better than in the absence of feedback.