Статьи журнала - Компьютерная оптика
Все статьи: 2553
Generation of linearly polarized modes using a digital micromirror device and phase optimization
Статья научная
Linearly polarized modes were generated from the fundamental LP01 using Lee holograms displayed on a digital micromirror device. The phase in the holograms was optimized using simulated annealing algorithm and complex amplitude correlation to improve the quality of the converted modes. The correlation measurements, and comparisons between numerical and experimental results, show the fidelity of the obtained modes and the effectiveness of the optimization. Furthermore, the optimized holograms can be combined to generate multiple modes spatially addressed with individual control. The results, and the use of a digital micromirror device instead of the most common liquid crystal modulators, make this method suitable for Modal Division Multiplexing systems and compatible with other optical telecommunication techniques like Wavelength and Polarization Division multiplexing, and reconfigurable optical networks.
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Genetic algorithm for optimizing Bragg and hybrid metal-dielectric reflectors
Статья научная
Highly efficient reflectors are in demand in the rapidly developing optoelectronics. At the moment, distributed Bragg reflectors made of semiconductor materials are mainly used in this capacity. A lot of time and financial resources are spent on their production. Reducing the thickness of the reflector while maintaining its reflectivity would make these devices more affordable and extend their lifetime by reducing thermal noise. With the help of genetic optimization algorithms, the structures of multilayer semiconductor and combined metal-semiconductor reflectors were obtained, having a smaller thickness and equal optical characteristics than those of classical analogues. In particular, a 29 % reduction in the thickness of the silicon / silica Bragg reflector was achieved without compromising performance.
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Gradient-based technique for image structural analysis and applications
Статья научная
This paper is devoted to application of gradients field characteristics in selected problems of image intellectual analysis and processing. To analyse the properties and structure of an image several approaches and models based on the use of the gradients field characteristics, are proposed. In this paper, models based on Weibull distribution are considered, an image dominant direction estimation algorithm using the parameters of scattering ellipse of gradients field components is proposed, and a similarity measure of two images with arbitrary dimensions and orientation is proposed. Some examples of applications of these models for estimation of blur and structuredness of an image, for the quality assessment of resizing and rotating algorithms, as well as for detection of a specified object on the image delivered by an unmanned aerial vehicle, are given.
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Handwritten text generation and strikethrough characters augmentation
Статья научная
We introduce two data augmentation techniques, which, used with a Resnet - BiLSTM - CTC network, significantly reduce Word Error Rate and Character Error Rate beyond best-reported results on handwriting text recognition tasks. We apply a novel augmentation that simulates strikethrough text (HandWritten Blots) and a handwritten text generation method based on printed text (StackMix), which proved to be very effective in handwriting text recognition tasks. StackMix uses weakly-supervised framework to get character boundaries. Because these data augmentation techniques are independent of the network used, they could also be applied to enhance the performance of other networks and approaches to handwriting text recognition. Extensive experiments on ten handwritten text datasets show that HandWritten Blots augmentation and StackMix significantly improve the quality of handwriting text recognition models.
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Head model reconstruction and animation method using color image with depth information
Статья научная
The article presents a method for reconstructing and animating a digital model of a human head from a single RGBD image, a color RGB image with depth information. An approach is proposed for optimizing the parametric FLAME model using a point cloud of a face corresponding to a single RGBD image. The results of experimental studies have shown that the proposed optimization approach makes it possible to obtain a head model with more prominent features of the original face compared to optimization approaches using RGB images or the same approaches generalized to RGBD images.
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High performance 2D simulations for the problem of optical breakdown
Статья научная
Methods of numerical simulation of two-dimensional short laser pulse nonlinear dynamics are discussed. In this work parallel processing methods for modern CPU (central processing units) architectures supporting non-uniform memory access are considered. The method of adaptive mesh subdivision is proposed to reduce non-uniform load of each CPU during processing of nonlinearity. The results of the tests performed on the Intel Nehalem based a workstation with eight cores are presented.
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High-Fidelity compression of 3D mesh animation data for humorous cartoon animation production
Статья научная
Humorous cartoon animation with its easy and pleasant style and colorful methods of expression, has become an important entertainment way for people to find relaxation and laughter in their busy lives. However, the data in the current humorous cartoon animation production is too complex. Therefore, the research proposes a new method based on high-fidelity compression algorithm, focusing on the special characteristics of 3D mesh animation data, and optimizing the compression from the two dimensions of time domain and space domain. The experimental results show that the proposed method exhibits higher compression ratio and rate, the average compression ratio reaches 2.55, and the compression rate reaches up to 65.34 Mb/s. It also exhibits lower mean squared deviation and high structural similarity index, the former is 1.56%, and the latter reaches up to 0.98. In the practical application, a compression effect of about 2:1 is achieved. Finally, in the volunteer rating of the produced humorous cartoon animation, the overall average score reaches 9.02. The study provides a new solution for the high-fidelity compression of 3D mesh animation data, which has the potential for practical application and is of great guiding significance for improving the efficiency and quality of animation production.
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Статья научная
The human olfactory bulb (OB) is a complex neural structure critical for odor processing and one of the earliest sites of pathology in a number of neurodegenerative diseases. We used X-ray phase-contrast tomography (XPCT) to obtain high-quality 3D images of OB tissue from postmortem patients, allowing detailed visualization of soft tissue microarchitecture, including the olfactory glomeruli. To improve spatial analysis, we developed a computational unfolding method that transforms the curved surface of the OB into a 2D map. This transformation preserves anatomical relationships, allowing accurate quantification of glomeruli by number, size, shape, and distribution. The unfolded representations of OB image support in-depth statistical analysis and are compatible with machine learning tools for automated detection and classification of OB morphological structures. This method provides a powerful framework for studying olfactory function and identifying early structural changes in diseases such as Parkinson's disease, Alzheimer's disease, and COVID-19-associated anosmia. By integrating XPCT with virtual unfolding, we offer a new approach to mapping OB morphological features with increased clarity and diagnostic accuracy.
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High-resolution X-ray imaging for industrial process monitoring and quality control
Статья научная
High-resolution X-ray imaging is an essential component of advanced workflows for industrial process monitoring and quality control (e.g., for metrology and defect inspection in the semiconductor industry). Depending on the specific application area, however, it is subject to different requirements, particularly regarding imaging accuracy and reconstruction fidelity, which are analyzed and systematically structured in this study. As an example, a seamless workflow of two nondestructive techniques with different spatial resolution and different throughput (here shown for a combination of acoustic and X-ray techniques) is proposed to auto-detect and auto-classify defects. X-ray microcopy and high-resolution X-ray computed tomography (XCT) provide nondestructive characterization capabilities on opaque objects, observing features with sizes down to several 10 nanometers. Because of the ability of micro-XCT and nano-XCT to reveal structural characteristics, to determine deviations from a well-defined standard, or to observe kinetic processes, they are suitable imaging techniques for micro- and nano-structured objects, but also for industrial process monitoring and quality control. Typical applications of high-resolution XCT are categorized into 3 groups: 1) Structure analysis – Creation of 3D digital images of the complete interior structure of an opaque object, 2) Flaw detection – Monitoring industrial processes and defect inspection, and 3) Quality control – Observing kinetic processes in objects important for industrial quality control and reliability engineering. These different categories of applications have different requirements for the accuracy of the 3D reconstruction and for the time-to-data. While the highest possible resolution is requested for group 1, data acquisition and data analysis time are essential for group 2. To get high-resolution 3D information of the complete interior structure of an opaque object using lens-based laboratory nano-XCT requires a thorough data analysis, e.g., the application of deep convolutional neural networks, for denoising and mitigation of artefacts. Kinetic studies for group 3, e.g., of reliability-limiting degradation processes in microchips, provide the opportunity to establish appropriate risk mitigation strategies to avoid catastrophic failure. The rapid evolution of advanced semiconductor technologies, including technologies for heterogeneous 3D integration of ICs and chiplet architectures, provides significant challenges for metrology, defect inspection, and physical failure analysis (PFA). The application of nano-XCT as a highly reliable inspection method requires a balance between throughput and fault detection (i.e., measurement and reconstruction accuracy). Ways to achieve a drastic increase in acquisition speed include high-brilliance laboratory X-ray sources, the application of AI algorithms for new image acquisition protocols, and high-speed data processing. A thorough and systematic analysis of the accuracy needed and the consequences for protocol and data analysis will support the goal of the semiconductor industry to improve throughput in metrology and defect inspection. This work may be of interest to a broad audience, including both specialists in the field of XCT and professionals employing XCT as a tool for industrial applications.
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High-speed recursive-separable image processing filters
Статья научная
The development of modern technologies in the field of image formation leads to an increase in the size of the generated images, as a result the question of reducing the processing computational costs arises, and this is an important factor in the creation of real-time systems. The study provides a description of high-speed recursive-separable filters for improving the quality of images, which, due to the peculiarities of their implementation, can reduce the number of computational operations required for the image processing process. This type of filters is obtained from two-dimensional linear digital filters, which are modified by applying recursive and separable properties to them. The MATLAB environment computing method for implementation of these filters is described. An extensive performance research of the developed filters has been carried out at various sizes of the test image and on various experimental installations. The comparison with the classical two-dimensional convolution method of the developed filters is demonstrated, and it shows the time gain required for the image processing. The results obtained can be applied in biomedical image processing systems or in vision systems working in heavy weather conditions.
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High-speed recursive-separable image processing filters with variable scanning aperture sizes
Статья научная
In the process of development of computer technologies, the number of areas of their application naturally grows and, along with it, the complexity of the tasks to be solved, which entails the need for new research. Similar tasks include digital filtering of images in the field of medical technologies and active-pulse television measuring systems. There are many methods and algorithms of digital filtering designed to solve the problem of improving the quality; algorithms that can improve the quality of images while reducing computational costs are widely used. High demands, which are made due to the constant growth in the size of the generated images, as well as the requirement for modern television systems, is real-time operation. When solving practical problems, it is required to use different filter aperture sizes, which provide an increase in quality and preservation of image details. The solution of these problems was the reason for the emergence of adaptive filters that are able to change the parameters in the process of processing the received data, while not spending additional time on processing with an increase in the size of the aperture. The paper presents the principles of constructing adaptive image processing filters, which, by obtaining an input parameter indicating the required dimension of a multi-element aperture, are able to implement the construction of the required aperture. The Laplacian “Truncated Pyramid” filter and the “double pyramid” Laplacian were modified. A feature of these filters is the oddness of the multi-element aperture, so the coefficient used to build the mask is always set to odd. When using these filters, it is possible to use two coefficients that are responsible for increasing the filtration efficiency, since, in their original form, the Laplacian filters have a sum of coefficients equal to zero. The experiment shows a comparison with high-dimensional filters that work when using classical two-dimensional convolution. The next stage of the presented research will be the application of parallel computing techniques, which will increase the speed of the developed filters.
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Human Action Recognition Based on The Skeletal Pairwise Dissimilarity
Статья научная
The main idea of the paper is to apply the principles of featureless pattern recognition to human activity recognition problem. The article presents the human figure representing approach based on pairwise dissimilarity function of skeletal models and a set of reference objects, also known as a basic assembly. The paper includes a basic assembly analysis and we propose the method for selecting the least-correlated basic objects. The video sequence proposed for analysis of human activity within frames is represented as an activity map. The activity map is a result of computing the pairwise dissimilarity function between skeletal models from the video sequence and the basic assembly of skeletons. The paper conducts frame-by-frame annotation of activities in the TST Fall Detection v2 database, such as standing, sitting, lying, walking, falling, post-fall lying, grasp, ungrasp. A convolutional neural network based on the ResNetV2 with the SE-block is proposed to solve the activity recognition problem. SE-block allows to detect inter-channel dependencies and selecting the most important features. Additionally, we prepare a data for training, determine an optimal hyperparameters of the neural network model. Experimental results of human activity recognition on the TST Fall Detection v2 database using the Leave-one-person-out procedure are provided. Furthermore, the paper presents a frame-by-frame assessment of the quality of human activity recognition, achieving an accuracy exceeding 83%.
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Hybrid Tamm-cavity modes in photonic crystal with resonant nanocomposite defect layer
Статья научная
Hybrid optical modes in a one-dimensional photonic crystal with a resonant nanocomposite defect bounded by a metallic layer are studied. The nanocomposite consists of spherical metallic constituents, that are distributed in a dielectric matrix. Transmittance, reflectance, and absorbance spectra of this structure, which is shined by light with normal incidence, are calculated. The possibility of control of the hybrid modes spectral characteristics by changing the thickness of the layer adjacent to the metal, the number of layers, and the nanocomposite filling factor is shown.
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Статья научная
Unsupervised segmentation of hyperspectral satellite images is a challenging task due to the nature of such images. In this paper, we address this task using the following three-step procedure. First, we reduce the dimensionality of the hyperspectral images. Then, we apply one of classical segmentation algorithms (segmentation via clustering, region growing, or watershed transform). Finally, to overcome the problem of over-segmentation, we use a region merging procedure based on priority queues. To find the parameters of the algorithms and to compare the segmentation approaches, we use known measures of the segmentation quality (global consistency error and rand index) and well-known hyperspectral images.
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Hyperspectral remote sensing data compression and protection
Статья научная
In this paper, we consider methods for hyperspectral image processing, required in systems of image formation, storage, and transmission and aimed at solving problems of data compression and protection. A modification of the digital image compression method based on a hierarchical grid interpolation is proposed. Methods of active (on the basis of digital watermarking) and passive (on the basis of artificial image distortion detection) data protection against unauthorized dissemination are developed and investigated.
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Illustration visual communication based on computer vision image retrieval algorithm
Статья научная
In illustration design, good visual communication can make the audience resonate. Computer vision image retrieval algorithm provides important support and assistance for the visual communication of illustration. However, the traditional image retrieval algorithm has problems of subjectivity and inaccuracy in complex image classification. Therefore, this paper optimizes the feature extraction module of convolutional neural network and fuses hash algorithm to improve the efficiency and speed of image retrieval. The experimental results show that the accuracy of the improved convolutional neural network is 82.7 %, which is more than 6 percentage points higher than the traditional algorithm model. The recall rate of the volume neural network model improved by hashing algorithm is 94.1 %. Research is of great significance to the visual communication of illustration design, which helps designers to find relevant materials more accurately, improve the artistic quality and ornamental value of their works, and promote the innovation and development of illustration design.
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Image compression and encryption based on wavelet transform and chaos
Статья научная
With the rapid development of network technology, more and more digital images are transmitted on the network, and gradually become one important means for people to access the information. The security problem of the image information data increasingly highlights and has become one problem to be attended. The current image encryption algorithm basically focuses on the simple encryption in the frequency domain or airspace domain, and related methods also have some shortcomings. Based on the characteristics of wavelet transform, this paper puts forward the image compression and encryption based on the wavelet transform and chaos by combining the advantages of chaotic mapping. This method introduces the chaos and wavelet transform into the digital image encryption algorithm, and transforms the image from the spatial domain to the frequency domain of wavelet transform, and adds the hybrid noise to the high frequency part of the wavelet transform, thus achieving the purpose of the image degradation and improving the encryption security by combining the encryption approaches in the spatial domain and frequency domain based on the chaotic sequence and the excellent characteristics of wavelet transform...
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Image compression using discrete orthogonal transforms with the «Noise-like» basis functions
Статья научная
The generalization of the discrete orthogonal transforms with the basis functions generated in a pseudorandom way is the subject of the article. The examples of such transforms application in the field of videoinformation coding in the channels with the high level of «seldom» noise are also given.
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Improvements of programing methods for finding reference lines on X-ray images
Статья научная
The paper gives an overview of the algorithms developed to obtain reference lines and angles on X-ray images. These geometrical characteristics are used in the medical analysis of human joints. We propose the algorithm’s modifications based on the analysis of numerous X-ray images. These modifications allowed obtaining a great increase in calculation speed and the improvement of final results quality given by the corresponding application. They also lead to a significant reduction of manual tuning of the program, arising only in the rare cases when the properties of given images differ significantly from the mean ones.
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Improving Data Matrix mobile recognition via fast Hough transform and adaptive grid extractors
Статья научная
The Data Matrix is a barcode symbology originally designed for industrial needs. Today, its symbols are increasingly found on everyday products such as pharmaceutical packaging, electronic components, food labels, and clothing tags. This widespread usage presents a challenge: reading Data Matrix symbols from images captured by mobile cameras in uncontrolled environments. The reading process mainly consists of three steps, namely barcode localization, segmentation and decoding. In this work, we focus on the precise localization and segmentation of Data Matrix barcodes. We introduce a new method that involves the localization of the finder pattern using fast Hough transform and subsequent iterative segmentation to extract the encoded message. Our approach demonstrates superior localization quality, as measured by the mean Intersection over Union metric (0.889), and achieves better recognition accuracy (0.903) compared to open–source solutions for reading Data Matrix barcodes, such as libdmtx (0.665), ZXing (0.569), and ZXing–cpp (0.858). Our method requires only 35 milliseconds for computations on an ARM device, enabling real–time processing. It is significantly faster than libdmtx (10 seconds), ZXing (610 milliseconds), although it is slightly slower than ZXing–cpp (6.65 milliseconds).
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