Персона:
Сафонов, Илья Владимирович

Организационные подразделения

Организационная единица

Институт общей профессиональной подготовки (ИОПП)

Миссией Института является: фундаментальная базовая подготовка студентов, необходимая для получения качественного образования на уровне требований международных стандартов; удовлетворение потребностей обучающихся в интеллектуальном, культурном, нравственном развитии и приобретении ими профессиональных знаний; формирование у студентов мотивации и умения учиться; профессиональная ориентация школьников и студентов в избранной области знаний, формирование способностей и навыков профессионального самоопределения и профессионального саморазвития. Основными целями и задачами Института являются: обеспечение высококачественной (фундаментальной) базовой подготовки студентов бакалавриата и специалитета; поддержка и развитие у студентов стремления к осознанному продолжению обучения в институтах (САЕ и др.) и на факультетах Университета; обеспечение преемственности образовательных программ общего среднего и высшего образования; обеспечение высокого качества довузовской подготовки учащихся Предуниверситария и школ-партнеров НИЯУ МИФИ за счет интеграции основного и дополнительного образования; учебно-методическое руководство общеобразовательными кафедрами Института, осуществляющими подготовку бакалавров и специалистов по социо-гуманитарным, общепрофессиональным и естественнонаучным дисциплинам, обеспечение единства требований к базовой подготовке студентов в рамках крупных научно-образовательных направлений (областей знаний).

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Сафонов

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Илья Владимирович

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Теперь показываю 1 - 10 из 13

Только метаданные
Blind quality assessment for slice of microtomographic image
(2019) Yakimchuk, I.; Kornilov, A.; Safonov, I.; Корнилов, Антон Сергеевич; Сафонов, Илья Владимирович
© 2019 FRUCT.The paper considers a new algorithm for blind quality assessment of a slice of X-ray microtomographic image. We selected the following factors impacting on micro-CT image quality with respect to Digital Rock technology: Smoothness, sharpness, contrast, absence of high-density regions and ring artifacts. We propose algorithms for estimation of partial quality measures for named factors inside Region-of-Interest, that is in area associated with a sample of rock or granular material. Total quality metrics is calculated as a product of these partial measures. Our method for quality assessment provides reasonable outcomes for synthetic and real slices of micro-CT images. We collected experts' judgments about quality of slices. Proposed solution has a high correlation with scores of experts and outperforms existing blind quality metrics. An application of developed method to all slices allows to obtain quality estimation for 3D micro-CT image.
Только метаданные
The regression model for the procedure of correction of photos damaged by backlighting
(2019) Goncharova, A. V.; Safonov, I. V.; Romanov, I. A.; Сафонов, Илья Владимирович
© 2019 CEUR-WS. All rights reserved.In the paper, we propose an approach for selection a correction parameter for images damaged by backlighting. We consider the photos containing underexposed areas due to backlit conditions. Such areas are dark and have poorly discernible details. The correction parameter controls the level of amplification of local contrast in shadow tones. Besides, the correction parameter can be considered as a quality estimation factor for such photos. For an automatic selection of the correction parameter, we apply regression by supervised machine learning. We propose new features calculated from the co-occurrence matrix for the training of the regression model. We compare the performance of the following techniques: the least square method, support vector machine, random forest, CART, random forest, two shallow neural networks as well as blending and staking of several models. We apply two-stage approach for the collection of a big dataset for training: initial model is trained on a manually labeled dataset containing about two hundred of photos, after that we use the initial model for searching for photos damaged by backlit in social networks having public API. Such approach allowed to collect about 1000 photos in conjunction with their preliminary quality assessments that were corrected by experts if it was necessary. In addition, we investigate an application of several well-known blind quality metrics for the estimation of photos affected by backlit.
Только метаданные
Natural Effect Generation and Reproduction
(2021) Kryzhanovskiy, K. A.; Safonov, I. V.; Сафонов, Илья Владимирович
© 2021, Springer Nature Switzerland AG.In this chapter, we describe an approach for automatic on-the-fly generation of audio- and content-adaptive animation effects from still images. Our method is intended to be adapted for implementation in embedded hardware platforms. Displayed animation behaves uniquely each time it is played back and does not repeat itself during playback, creating vivid and lively impressions for the viewer. Adaptation of effect parameters according to the background audio greatly increases the aesthetic impressions of the viewer. Three animation effects – flashing light, soap bubbles, and sunlight spot – are described in detail. We propose several ways to control the effect parameters using music. A user-opinion survey demonstrates that the majority of users are excited by such effects and would like to see these features in modern gadgets.
Только метаданные
Deep neural networks for ring artifacts segmentation and corrections in fragments of CT images
(2021) Reimers, I.; Yakimchuk, I.; Kornilov, A.; Safonov, I.; Корнилов, Антон Сергеевич; Сафонов, Илья Владимирович
© 2021 IEEE Computer Society. All rights reserved.Ring artifacts are typical defects of computed tomography (CT) that degrade the quality of a 3D reconstructed image. Existing techniques for a ring reduction have various shortcomings and limitations, in particular, a lot of them are unable to process arbitrary fragments of the image and blur artifact-free regions. We propose an algorithm for ring artifacts segmentation and reduction by deep convolutional neural networks that correct 3D fragments of the CT image by inpainting. We compare 2D and 3D architectures of networks. For the creation of a dataset with a big number of ring artifacts, we propose a procedure that is able to transfer an artifact from one image to an arbitrary place of another image. The appearance of the transferred artifact changes. For ring artifact segmentation and correction in images of sandstones and sand, the proposed networks demonstrate good visual results and outperform existing methods. The proposed technique concentrates on the Digital Rock workflow, but the networks can be adjusted for the processing of other CT images as well.
Только метаданные
Rendering semisynthetic FIB-SEM images of rock samples
(2021) Reimers, I.; Safonov, I.; Kornilov, A.; Сафонов, Илья Владимирович; Корнилов, Антон Сергеевич
© 2021 Copyright for this paper by its authors.Digital rock analysis is a prospective approach to estimate properties of oil and gas reservoirs. This concept implies constructing a 3D digital twin of a rock sample. Focused Ion Beam - Scanning Electron Microscope (FIB-SEM) allows to obtain a 3D image of a sample at nanoscale. One of the main specific features of FIB-SEM images in case of porous media is pore-back (or shine-through) effect. Since pores are transparent, their back side is visible in the current slice, whereas, in fact, it locates in the following ones. A precise segmentation of pores is a challenging problem. Absence of annotated ground truth complicates fine-tuning the algorithms for processing of FIB-SEM data and prevents successful application of machine-learning-based methods, which require a huge training set. Recently, several synthetic FIB-SEM images based on stochastic structures were created. However, those images strongly differ from images of real samples. We propose fast approaches to render semisynthetic FIB-SEM images, which imply that intensities of voxels of mineral matrix in a milling plane, as well as geometry of pore space, are borrowed from an image of rock sample saturated by epoxy. Intensities of voxels in pores depend on the distance from milling plane to the given voxel along a ray directed at an angle equal to the angle between FIB and SEM columns. The proposed method allows to create very realistic FIB-SEM images of rock samples with precise ground truth. Also, it opens the door for numerical estimation of plenty of algorithms for processing FIB-SEM data.
Только метаданные
An Animated Graphical Abstract for an Image
(2021) Reimers, I. A.; Safonov, I. V.; Kornilov, A. S.; Сафонов, Илья Владимирович; Корнилов, Антон Сергеевич
© 2021, Springer Nature Switzerland AG.The conventional approach used in modern user interfaces for browsing of a big collection of images by viewing their downsampled versions often does not allow to recognise the content and to assess the quality of the images. To cope with this problem, we propose a method for the creation of an animated graphical abstract. Our approach comprises detection of attention zones, selection of the region for visual quality estimation, and generation of animation that simulates the camera tracking-in, tracking-out, and panning between detected zones and the whole scene. The short looped animation shows the whole image, main objects of the scene one by one, as well as an enlarged fragment for estimation quality of the image. We demonstrate our concept for consumer photographs, scanned images of documents, and slices of tomographic images. The attention zones vary by the type of image. Face detection and salient regions are used for photos. The title, subheaders, and pictures are attention zones for document images. The attention zones in slices of tomographic images in the investigation of rock samples are enlarged regions of a material having different characteristics according to visual similarity. We conducted five user studies to prove the effectiveness of the proposed animated graphical abstract in comparison with conventional thumbnails and icons.
Только метаданные
A Review of Watershed Implementations for Segmentation of Volumetric Images
(2022) Yakimchuk, I.; Kornilov, A.; Safonov, I.; Корнилов, Антон Сергеевич; Сафонов, Илья Владимирович
© 2022 by the authors. Licensee MDPI, Basel, Switzerland.Watershed is a widely used image segmentation algorithm. Most researchers understand just an idea of this method: a grayscale image is considered as topographic relief, which is flooded from initial basins. However, frequently they are not aware of the options of the algorithm and the peculiarities of its realizations. There are many watershed implementations in software packages and products. Even if these packages are based on the identical algorithm–watershed, by flooding their outcomes, processing speed, and consumed memory, vary greatly. In particular, the difference among various implementations is noticeable for huge volumetric images; for instance, tomographic 3D images, for which low performance and high memory requirements of watershed might be bottlenecks. In our review, we discuss the peculiarities of algorithms with and without waterline generation, the impact of connectivity type and relief quantization level on the result, approaches for parallelization, as well as other method options. We present detailed benchmarking of seven open-source and three commercial software implementations of marker-controlled watershed for semantic or instance segmentation. We compare those software packages for one synthetic and two natural volumetric images. The aim of the review is to provide information and advice for practitioners to select the appropriate version of watershed for their problem solving. In addition, we forecast future directions of software development for 3D image segmentation by watershed.
Открытый доступ
Analysis of Open Well Datasets
(НИЯУ МИФИ, 2024) Makienko, D. O.; Safonov, I. V.; Сафонов, Илья Владимирович
Recently, the number of studies devoted to the use of machine learning methods in geophysics has been increasing significantly. Examples of such investigations include the prediction of rock properties and separation of rock types according to quantitative characteristics. Annotated datasets are required to build and evaluate the quality of machine learning based models. This paper analyzes open labeled well datasets and related research. We consider data containing well logs, rock images, laboratory results, labeled zonation by lithotypes. Methods for visualizing well data are presented. We provide recommendations for oil and gas companies on the preferable format for making well data publicly available
Только метаданные
Analysis of Open Well Datasets
(2024) Makienko, D. O.; Safonov, I. V.; Сафонов, Илья Владимирович
Открытый доступ
An Approach for Matrix Multiplication of 32-Bit Fixed Point Numbers by Means of 16-Bit SIMD Instructions on DSP
(2023) Safonov, I.; Kornilov, A.; Makienko, D.; Сафонов, Илья Владимирович; Корнилов, Антон Сергеевич; Макиенко, Дарья Олеговна