Статьи журнала - Компьютерная оптика
Все статьи: 2609
Asymmetric apodization for the comma aberrated point spread function
Статья научная
This paper deals with the study of light flux distributions in the point spread function formed by an optical system with a one-dimensional aperture under the influence of the coma aberration. The traditional design of an asymmetric optical filter improves the resolution of a diffraction-limited optical imaging system. In this approach we explore the control of monochromatic aberrations through pupil engineering with asymmetric apodization. This technique employs the amplitude and phase apodization for the mitigation of the effects of third-order aberrations on the diffracted image. On introducing the coma wave aberration effect, the central peak intensity in the field of diffraction is a function of the edge strips width and the amplitude apodization parameter of a one-dimensional pupil filter, whereas the magnitude of the reduction of optical side-lobes is a function of the degree of phase apodization at the periphery of the aperture. The analytically computed results are illustrated graphically in terms of point spread function curves under various considerations of the coma aberrations and a different degree of amplitude and phase apodization. Hence, for the optimum values of apodization, the axial resolution has been analyzed using well-defined quality criteria.
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Automated recursive separable algorithm for medical image processing
Статья научная
In the modern world, there is an increasing demand for automated information processing systems that reduce the time spent on monotonous human work, increase the efficiency of algorithms and the time it takes to solve assigned tasks. The paper presents the development of an automated recursive-separable digital filter for processing digital medical images. The feature of the automated algorithm is its speed; due to its internal structure, the recursive-separable Laplacian Filter “double pyramid” performs separate processing by row and column of the image, using recirculators. The recirculator itself also affects the speed due to the use of recursion properties. Automation of the algorithm will increase the efficiency of digital image processing, due to automatic enumeration of filter coefficients, which will improve the quality of the original information almost without human intervention. The developed algorithm itself carries out the process of image quality assessment and repeats the digital processing procedure until it reaches the required values of one of the parameters: mean square error (MSE), peak signal-to-noise ratio (PSNR) and structural similarity index of image (SSIM). To test the developed automated algorithm, the software "ALF: Automated LDP Filter" was developed and implemented using QtDesigner and PySide6. The software module consists of three data entry areas, such as the mask size h, the coefficient for lifting A1 and the coefficient for increasing the central element A2, two areas for displaying the image, the original and processed, seven buttons, such as input, output and saving the image, checking the image quality condition, three buttons for calculating the parameters MSE, PSNR and SSIM. As part of the testing, a study was carried out on the performance of the automated recursive separable digital filter using the example of endoscopic images obtained from a robotic surgical complex, which showed the effectiveness of its use, since it was possible to achieve the specified parameter values on the output image.
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Automatic 4-mirrors system for alignment of high-power laser radiation
Статья научная
This paper presents the automated system for minimizing the deviation of the path of passage and the divergence of a secondary radiation source with parameters similar to ones of the main beam of a high-power Ti:Sa laser using mirrors in kinematic mounts on the motorized stages. As an alignment laser, the diode laser with a fiber output was used with radiation characteristics coinciding with the parameters of the main beam (wavelength, beam diameter). The successive approximation algorithm was used to minimize the beam deflection. The positioning accuracy and beam size matching were analyzed on the near-field camera and were equaled to 28.6 µm along the X-axis and 26.4 µm along the Y-axis. Beam size mismatch was equaled to 0.151 mm. The pointing accuracy was analyzed on the far-field sensor and equaled 15.34 µrad along the X axis and 12.03 µrad along the Y axis. The curvature of the wavefront was 0.06 µm.
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Automatic mosaic method of remote sensing images based on machine vision
Статья научная
Unmanned Aerial Vehicle (UAV) remote sensing is a commonly used technical means in modern science and technology, but currently, remote sensing images captured by UAVs need to be spliced to obtain more comprehensive information. However, current image stitching techniques generally have shortcomings such as a small number of extracted features, low matching accuracy, and poor stability. To address the above issues, this study proposes an improved remote sensing image mosaic model on the bias of the Scale Invariant Feature Transform (SIFT) algorithm. Firstly, in this study, aiming at the problem that traditional SIFT cannot meet the requirements of feature extraction and matching for unconventional remote sensing images and special texture images, normalized cross correlation (NCC) and Forstner operator are introduced to optimize it, namely, a SIFT-NCC model is constructed. On this basis, for remote sensing images with high resolution and a wide range, this study further proposes a remote sensing image automatic mosaic model that combines point features and line features. That is, a linear segment detector (LSD) is introduced to extract the line features of remote sensing images. The experimental verification results of the final SIFT-NCC-LSD show that the matching accuracy for remote sensing images with different characteristics can reach over 95 %. Therefore, SIFT-NCC-LSD has good applicability.
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Automatic target recognition algorithm for low-count terahertz images
Статья научная
The paper presents the results of developing an algorithm for automatic target recognition in broadband (0.1-10) terahertz images. Due to the physical properties of terahertz radiation and associated hardware, such images have low contrast, low signal-to-noise ratio and low resolution - i.e. all the characteristics of a low-count images. Therefore, standard recognition algorithms designed for conventional images work poorly or are not suitable at all for the problem considered. We have developed a fundamentally different approach based on clustering 2D point clouds in accordance with a set of predefined patterns. As a result, we reduce the problem of target recognition to the problem of maximizing the image data likelihood with respect to the classes of model objects up to the size and position. The resulting recognition algorithm has a structure close to that of the well-known EM algorithm; its formal scheme is at the end of the paper.
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Статья научная
Breast cancer is a leading cause of death in women due to cancer. According to WHO, in 2018, it is estimated that 627.000 women died from breast cancer, that is approximately 15 % of all cancer deaths among women [3]. Early detection is a very important factor to reduce mortality by 25 - 30 %. Mammography is the most commonly used technique in detecting breast cancer using a low-dose X-ray system in the examination of breast tissue that can reduce false positives. A Computer-Aided Detection (CAD) system has been developed to effectively assist radiologists in detecting masses on mammograms that indicate the presence of breast tumors. The type of abnormality in mammogram images can be seen from the presence of microcalcifications and the presence of mass lesions. In this research, a new approach was developed to improve the performance of CAD System for classifying benign and malignant tumors. Areas suspected of being masses (RoI) in mammogram images were detected using an adaptive thresholding method and mathematical morphological operations. Wavelet decomposition is performed on the Region of Interest (RoI) and the feature extraction process is performed using a GLCM method with 4 statistical features, namely, contrast, correlation, entropy, and homogeneity. Classification of benign and malignant tumors using the MIAS database provided an accuracy of 95.83 % with a sensitivity of 95.23 % and a specificity of 96.49 %. A comparison with other methods illustrates that the proposed method provides better performance.
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Статья научная
This paper proposes a modern system for recognizing sunflower diseases based on Bidirectional Encoder representation from Image Transformers (BEIT). The proposed system is capable of recognizing various sunflower diseases with high accuracy. The presented research results demonstrate the advantages of the proposed system compared to known methods and contemporary neural networks. The proposed visual diagnostic system for sunflower diseases achieved 99.57 % accuracy on the sunflower disease dataset, which is higher than that of known methods. The approach described in the work can serve as an auxiliary tool for farmers, assisting them in promptly identifying diseases and pests and taking timely measures to treat plants. This, in turn, helps in preserving and enhancing the yield. This work can have a significant impact on the development of agriculture and the fight against the global food shortage problem.
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Bragg-fresnel optics and supermirrors
Статья научная
The main principles and some applications of Bragg-Fresnel multilayer optics and X-ray supermirrors are described. An elliptical Bragg-Fresnel multilayer lens (BFML), designed and fabricated in the IMT RAS has been used for 2-dimensional focusing of the white X-ray synchrotron beam. For the beam energy of about 12 KeV the spot size checked with the knife edge method was about 1 mm. Applications of BFML and supermirrors in x-ray imaging are discussed.
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Brain tumor classification using deep convolutional neural networks
Статья научная
This study presents a comparative analysis of various convolutional neural network (CNN) models for brain tumor detection on MRI medical images. The primary aim was to assess the effectiveness of different CNN architectures in accurately identifying brain tumors. Multiple models were trained, including a custom-designed CNN with its specific layer architecture, and models based on Transfer Learning utilizing pre-trained neural networks: ResNet-50, VGG-16, and Xception. Performance evaluation of each model in terms of accuracy metrics such as precision, recall, F1-score, and confusion matrix on a test dataset was carried out. The dataset used in this study was obtained from the openly accessible Kaggle competition "Brain Tumor Detection from MRI." This dataset consisted of four classes: glioma, meningioma, no tumor (healthy), and pituitary, ensuring a balanced representation. Testing four models revealed that the custom CNN architecture, utilizing separable convolutions and batch normalization, achieved an average ROC AUC score of 0.99, outperforming the other models. Moreover, this model demonstrated an accuracy of 0.94, indicating its robust performance in brain tumor classification on MRI images.
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Brain tumor segmentation by deep learning transfer methods using MRI images
Статья научная
Brain tumor segmentation is one of the most challenging tasks of medical image analysis. The diagnosis of patients with gliomas is based on the analysis of magnetic resonance images and manual segmentation of tumor boundaries. However, due to its time-consuming nature, there is a need for a fast and reliable automatic segmentation algorithm. In recent years, deep learning methods applied to brain tumor segmentation have shown promising results. In this paper, a deep neural network model based on U-Net neural network architecture is proposed for brain glioma segmentation. It is proposed to use deep convolutional neural network models pre-trained on the ImageNet dataset as U-Net encoders. Among such models, VGG16, VGG19, Mobilenetv2, Inception, Efficientnetb7, InceptionResnetV2, DenseNet201, DenseNet121 were used. The computational experimental analysis performed in the paper on a set of MRI brain images showed that the best encoder model for the above deep models was the DenseNet121 model with the following values of segmentation metrics Mean IoU of 91.34 %, Mean Dice of 94.26 %, Accuracy of 94.22 %. The paper also comparatively analyses the results of the proposed segmentation method with several works of other authors. The comparative analysis of the segmentation results of the studied MRI images showed that the DenseNet121 model either surpassed or was comparable to the models proposed in the refereed papers in terms of segmentation accuracy metrics.
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Building detection by local region features in SAR images
Статья научная
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.
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Bulk cargo volume measurement for moving dump trucks with a single-layer LiDAR and a camera
Статья научная
The paper addresses the problem of non–contact bulk cargo volume estimation for moving dump trucks. A common scanning method that lets to evaluate the volume of cargo of complex surface for a moving truck implies two single-layer (2D) Light Detection and Ranging (LiDAR) sensors: one is used to scan a vehicle in a plane perpendicular to its movement and the second – to estimate vehicle displacements and restore scans positions on an axis along vehicle movement direction. While LiDAR sensors provide reliable measurement signals in controlled environments their efficacy drastically decreases under challenging outdoor conditions: sand dust, fog, rain heavy precipitation cause false detections and distort LiDARs signal. Thus, vehicle displacements estimated with a highly corrupted LiDAR signal can not be used for a reliable measurement as they may lead to significant volume calculation errors. Partially this is solved in multi-echo lidar where distorted data could be separated from the relevant. In contrast to the single-echo 2D LiDAR, image data from industrial cameras is less sensitive to sand dust or fog. In the paper we propose a novel bulk cargo estimation method that implies only one 2D LiDAR and for vehicle displacements estimation utilizes a camera and computer vision methods. As we demonstrate on a diverse dataset of 730 pairs of dump truck passes from an operating sand pit, the proposed method is more accurate than the two 2D LiDARs baseline while requiring a significantly cheaper sensor. In case if a camera is already present in the volume measurement system and utilized for loaded material classification then the proposed method lets to reduce the cost of solution by the cost of one lidar.
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Статья научная
Photonic crystal fiber as a new type of optical fiber has been extensively applied because of its unique properties. The effective mode area of optical fiber is an important parameter, which has a great influence on the performance of optical fiber. In this study, digital image processing algo-rithm was used for preprocessing to improve the accuracy of calculation of mode field area. Then the effective mode field area of optical fiber was calculated using Matlab based Gauss fitting method. Take single-mode fiber G.652 as an example, the effective mode field area was calculated using the traditional algorithm and digital image processing algorithm respectively. It was found that the results obtained using digital image processing algorithm were within the allowed error range, suggesting the effectiveness of the algorithm. Then the calculation of the effective mode area of the triangular lattice photonic crystal fiber further verified the reliability of the algorithm.
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Camera parameters estimation from pose detections
Статья научная
Some computer vision tasks become easier with known camera calibration. We propose a method for camera focal length, location and orientation estimation by observing human poses in the scene. Weak requirements to the observed scene make the method applicable to a wide range of scenarios. Our evaluation shows that even being trained only on synthetic dataset, the proposed method outperforms known solution. Our experiments show that using only human poses as the input also allows the proposed method to calibrate dynamic visual sensors.
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Central Russia heavy metal contamination model based on satellite imagery and machine learning
Статья научная
Atmospheric heavy metal contamination is a real threat to human health. In this work, we examined several models trained on in situ data and indices got from satellite images. During 2018-2019, 281 samples of naturally growing mosses were collected in the Vladimir, Yaroslavl, and Moscow regions in Russia. The samples were analyzed using Neutron Activation Analysis to get the contamination levels of 18 heavy metals. The Google Earth Engine platform was used to calculate indices from satellite images that represent summarized information about sampling sites. Statistical and neural models were trained on in situ data and the indices. We focused on the classification task with 8 levels of contamination and used balancing techniques to extend the training data. Three approaches were tested: variations of gradient boosting, multilayer perceptron, and Siamese networks. All these approaches produced results with minute differences, making it difficult to judge which one is better in terms of accuracy and graphical outputs. Promising results were shown for 9 heavy metals with an overall accuracy exceeding 89 %. Al, Fe, and Sb contamination was predicted for 3,000 and 12,100 grid nodes on a 500 km2 area in the Central Russia region for 2019 and 2020. The results, methods, and perspectives of the adopted approach of using satellite data together with machine learning for HM contamination prediction are presented.
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Статья научная
The amount of ultrasound (US) breast exams continues to grow because of the wider endorsement of breast cancer screening programs. When a solid lesion is found during the US the primary task is to decide if it requires a biopsy. Therefore, our goal was to develop a noninvasive US grayscale image analysis for benign and malignant solid breast lesion differentiation. We used a dataset consisting of 105 ultrasound images with 50 benign and 55 malignant non-cystic lesions. Features were extracted from the source image, the image of the gradient module after applying the Sobel filter, and the image after the Laplace filter. Subsequently, eight gray-level co-occurrence matrices (GLCM) were constructed for each lesion, and 13 Haralick textural features were calculated for each GLCM. Additionally, we computed the differences in feature values at different spatial shifts and the differences in feature values between the inner and outer areas of the lesion. The LASSO method was employed to determine the most significant features for classification. Finally, the lesion classification was carried out by various methods. The use of LASSO regression for feature selection enabled us to identify the most significant features for classification. Out of the 13 features selected by the LASSO method, four described the perilesional tissue, two represented the inner area of the lesion and five described the image of the gradient module. The final model achieved a sensitivity of 98%, specificity of 96%, and accuracy of 97%. Considering the perilesional area, Haralick feature differences, and the image of the gradient module can provide crucial parameters for accurate classification of US images. Features with a low AUC index (less than 0.6 in our case) can also be important for improving the quality of classification.
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Coherent field phase retrieval using a phase Zernike filter
Статья научная
Aberrations of the coherent wavefront are analyzed using a phase Zernike filter. Developed iterative methods allow us to design a filter that decomposes the analyzed light field into a set of diffraction orders with amplitudes proportional to the circular Zernike polynomials. We also apply the algorithm to the calculation of the light field phase from measurements of the modules of decomposition coefficients. Operation of a 25-channel filter is simulated.
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Статья научная
In this study, an algorithm based on convolutional neural networks is employed as an interrogation method for a fiber specklegram sensor. This algorithm is compared with conventional interrogation methods, including correlation between images, measurement of optical power, and radial moments. Fiber specklegram sensors have room for improvement as conventional methods only consider a single characteristic of the specklegram for variable prediction, thus failing to leverage the full spectrum of information within the specklegram. Consequently, the approach put forth here introduces a convolutional neural network for the extraction of specklegram features, accompanied by an artificial neural network for variable regression. The specklegrams used in this investigation are obtained through simulating temperature disturbances in a multimode fiber using the Finite Elements Method. The results reveal prediction RMSE errors ranging from 10.26°C for the first radial moment to 1.42°C for the proposed algorithm. These findings underscore the effectiveness of the proposed strategy in enhancing sensor performance and robustness, all while upholding their cost-efficiency.
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Статья научная
A computer vision based real-time object detection on low-power devices is economically attractive, yet a technically challenging task. The paper presents results of benchmarks on popular deep neural network models, which are often used for this task. The results of experiments provide insights into trade-offs between accuracy, speed, and computational efficiency of MobileNetV2 SSD, CenterNet MobileNetV2 FPN, EfficientDet, YoloV5, YoloV7, YoloV7 Tiny and YoloV8 neural network models on Raspberry Pi 4B, Raspberry Pi 3B and NVIDIA Jetson Nano with TensorFlow Lite. We fine-tuned the models on our custom dataset prior to benchmarking and used post-training quantization (PTQ) and quantization-aware training (QAT) to optimize the models’ size and speed. The experiments demonstrated that an appropriate algorithm selection depends on task requirements. We recommend EfficientDet Lite 512×512 quantized or YoloV7 Tiny for tasks that require around 2 FPS, EfficientDet Lite 320×320 quantized or SSD Mobilenet V2 320×320 for tasks with over 10 FPS, and EfficientDet Lite 320×320 or YoloV5 320×320 with QAT for tasks with intermediate FPS requirements.
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Статья научная
The study is a comparative analysis of two fast reflection symmetry axis detection methods: an algorithm to refine the symmetry axis found with a chain of skeletal primitives and a boundary method based on the Fourier descriptor. We tested the algorithms with binary raster images of plant leaves (FLAVIA database). The symmetry axis detection quality and performance indicate that both methods can be used to solve applied problems. Neither method demonstrated any significant advantage in terms of accuracy or performance. It is advisable to integrate both methods for solving real-life problems.
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