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• IMPROVING REAL PERFORMANCE OF RCS WHEN SOLVING DIGITAL IMAGE PROCESSING TASKS USING FAST FOURIER TRANSFORM

  A.V. Chkan

  2021-02-25

  Abstract ▼

  The article discusses the issues of digital processing of images of large dimensions in real
  time using reconfigurable computer systems (RCS) on the base of programmable logic arrays
  (FPGAs). RCS belongs to the class of high-performance multiprocessor computing systems that
  have a programmable architecture that allows configuring the structure of a computer system and
  optimally adjusting it to the algorithms of the solved task. At the same time, optimization of the
  computational structure of the task reduced to the development and implementation of parallel
  algorithms corresponding to the specifics of the RCS architecture used. All this allows to effectively
  using RCS to solve a wide class of digital signal processing tasks. Offered are methods of increasing
  specific and real performance of RCS when solving digital image processing problems
  using fast Fourier transform (FFT). Using the example of a procedure for filtering images in the
  frequency domain, the main computational steps and methods for optimizing them based on the
  properties of the FFT algorithm are discussed. The use of optimization allows to significantly reducing
  both the amount of computation and the amount of hardware resources of the FPGA andincrease the performance of RCS for image processing tasks. The FPGA resources freed because
  of the optimization of the computational structure can be uses to further parallelize calculations
  and accelerate the processing of incoming data. The advantages of presenting data in fixed-point
  format when performing calculations on RCS are showed. The use of a fixed point allows not only
  to increase the specific and real performance of a computer system compared to a floating point
  due to the properties of the format, but also to use arbitrary data bit capacity, which is relevant for
  most digital signal processing tasks. The solution to the problem of overflow of the bit grid when
  using the fixed-point format using data bit scaling is discussed.

• METHOD FOR DETECTING FEATURE POINTS OF AN IMAGE USING A SIGN REPRESENTATIONS

  A. N. Karkishchenko, V. B. Mnukhin

  2020-11-22

  Abstract ▼

  The aim of the study is to develop a method for detecting feature points of a digital image
  that is stable with respect to a certain class of brightness transformations. The need for such a
  method is due to the needs of detecting feature points of images in video surveillance systems and
  face recognition, often working in a changing light environment. A feature of the proposed method
  that distinguishes it from a number of well-known approaches to the problem of distinguishing
  characteristic points is the use of the so-called sign representation of images. In contrast to the
  usual defining of a digital image by a discrete brightness function, with a sign representation, the
  image is set in the form of an oriented graph corresponding to the binary relation of the increase
  in brightness on a set of pixels. Thus, the sign representation determines not a single image, but a
  set of images, the brightness functions of which are connected by strictly monotonic brightness
  transformations. It is this property of the sign representation that determines its effectiveness for
  solving the problems caused by the goal set above. A feature of the method under consideration is
  a special approach to the interpretation of the characteristic points of the image. This concept in
  image processing theory is not strictly defined; we can say that the characteristic point is characterized
  by increased "complexity" of the image structure in its vicinity. Since the sign representation
  of the image can be represented in the form of a directed graph, in this paper, to evaluate the
  complexity measure of the local neighborhood of its vertices, it is proposed to use the ranking
  method known in the spectral theory of graphs based on the Perron-Frobenius theorem. Its essence
  lies in the fact that the value of the component of the so-called Perron eigenvector of the
  adjacency matrix of this graph acts as a measure of the complexity of the vertex. To conduct experimental
  studies of the proposed approach, a set of programs was developed, the results of
  which confirm the efficiency of the method and demonstrate that with its help it is possible to obtain
  results close to the expected ones on model examples. The paper also offers a number of recommendations
  on the use of this method.

• MULTI-AGENT SYSTEM USING ARTIFICIAL INTELLIGENCE TO PROCESS IMAGES FROM THE DRONE'S TECHNICAL VISION CAMERAS

  А. L. Verevkin , I.E. Josephs , V.V. Misyura , L.S. Verevkina

  198-212

  2025-07-24

  Abstract ▼

  Multi-agent technology with drones, modern sensors, precise GPS and artificial intelligence, have led to a breakthrough in the field of cyber-physical systems. This article presents a multi-agent system using artificial intelligence to process images from technical vision cameras installed on a drone. A block diagram of a multi-agent system on a drone was developed based on an effective and simple platform taken from the ARRISE 410 octocopter – an agricultural sprayer drone with: intelligent control system; omnidirectional digital microwave radar; 6-axis high-precision accelerometer; electronic level for measuring tilt; real-time optical camera 1 with a first-person view; control panel equipped with the latest Light Bridge 2 signal transmission system; remote control has a design protected from dust and water. The kit must be supplemented with: hyperspectral HS - camera for scanning, its power module and the ability to interface with the ARRISE 410 drone systems, an information compression module. Model for studying the throughput on the DJI Agras T20 hexacopter DJI Agras T20, MikrotikRB411 5G network card, Raspberry Pi 3 microcomputer, 1 Mpix RGB camera, built-in on-board computer Raspberry Pi OV5647 v1.3 and hyperspectral HS - camera 2 Resonon Pika L shoots hyperspectral data with 281 spectral bands with spectral wavelengths from 400 to 1000 nm and a spatial resolution of 900 hyperspectral pixels per image line. The article solves the problem of experimentally and computationally determining the required compression of information obtained from hyperspectral and optical range cameras with transmission through a telecom operator and the Internet for image processing by an artificial Internet

• ALGORITHM FOR PRE-PROCESSING VIDEO IMAGES TO INCREASE THE ACCURACY OF SMALL OBJECT DETECTION

  V.V. Kovalev, N.E. Sergeev

  2021-12-24

  Abstract ▼

  Recognition of certain patterns in video images captured by a camera is carried out using
  training methods based on convolutional neural networks. The larger the number of images with
  multiple features and the more diverse the training sample of video images, the better the convolutional
  neural networks extract features from the sequence of video images that were not included in
  the training sample. This is a consequence of increasing the accuracy of detecting visual images on
  video images containing features of target images. However, there are limitations in improving the
  detection performance when the size of the image to be detected is much smaller than the background
  area, or when the image is described with little information. To solve problems of this kind, the authors
  of the article have developed an algorithm for the spatio-temporal integration of information
  about the movement of dynamic images. The algorithm processes a fixed number of video images at
  certain points in time and extracts new independent signs of motion of dynamic images based on
  space-time processing of video images. Further, it combines new local motion features with the original
  video image features. This allows you to add a motion feature of dynamic images while preserving
  the original image features that describe static images. Areas of the video image that characterize
  the motion feature are displayed in a «color» cluster. The use of pre-processing is aimed at improving the accuracy of pattern detection, provided there are dynamic visual images on a static background.
  If the camera is in scan mode, a static background can be provided with a video stabilizer.
  Experimentally, estimates of integral criteria for the accuracy of detection neural network algorithms
  have been obtained, showing an increase in the accuracy of detecting visual images using
  the algorithm for spatial-temporal integration of motion information.

• COMPARATIVE ANALYSIS OF TWO FILTERING METHODS TO ELIMINATE NOISE IN AN IMAGE OF DIFFERENT DEGREES OF NOISE

  K.O. Sever, I.I. Turulin, D.A. Guzhva

  2021-08-11

  Abstract ▼

  In modern photography and video technology, any image in the process of its creation is
  distorted by various types of noise. There are various types of noise, but in practice, impulsive and
  Gaussian noise models are the most common. Attenuation of the effect of noise is achieved by filtering.
  At the moment, there is no universal filter that suppresses noise data at various intens ities
  of distortion. Therefore, an important aspect is to determine the field of application of each
  type of filter when suppressing noise in the image and creating a filter, consisting of a combination
  of different filtering methods for optimal image cleaning. The article presents a comparative
  analysis of median filtering and Wiener filtering to eliminate impulse and Gaussian noise in
  the image with different degrees of noise. For modeling, we used one image, separately distorted
  by impulse and separately by Gaussian noise with pixel distortion probabilities from 1% to
  99% inclusive. Filtration was performed with windows equal to 3x3 and 5x5. As a result, we
  obtained numerical estimates of the image filtering quality based on the peak signal-to-noise
  ratio (PSNR). On the basis of the data obtained, the application of the investigated filters, their
  modifications, advantages and disadvantages were analyzed, as well as recommendations for
  their use were given. As a result of a comparative analysis of the studied types of filtering for
  noisy images, it was found that the median filter with a 3x3 window copes better with image
  cleaning from low-intensity impulse noise and with a 5x5 window - with image cleaning with an
  average noise intensity. Also, the median filter does a better job of filtering out Waussian noise
  at its medium and high rms deviations. The Wiener filter with 3x3 and 5x5 windows better fi lters
  Gaussian noise at small values of its root-mean-square deviation. Also, the Wiener filter
  copes better with impulse noise with relatively high noise power.

• PROSPECTS OF MALE-CLASS UAVS USING FOR THE HUGE TERRITORIES AERIAL SURVEY

  А. М. Fedulin, D.M. Driagin

  2021-04-04

  Abstract ▼

  The aim of the study is to estimate the MALE-class (Medium Altitude Long Endurance) UAV
  (Unmanned Air Vehicles) using possibility to solve the problem of regular aerial survey of huge
  areas relative to other means used for this, such as: small-sized UAVs, satellite remote sensing
  and manned aircrafts. Considered is the issue of practical construction of onboard computer vision
  system based on a UAV “Orion” wit a ta eoff weig t of more t an a ton, w ic pro ides
  aerial photography in the visible and near infrared range and airborne laser scanning of the underlying
  surface with automatic processing of the received data on board in near real-time mode
  detecting the changes occurred since the previous survey. It has been determined the key components
  of the computer vision system both the hardware and software platform required highperformance
  computing and big-data storage. It has been presented a promising architecture,
  given estimates for its search performance, weight and power consumption, determined the typical
  flight altitude, which provides the input data spatial resolution, which is necessary for objectoriented
  change detection algorithms, based on a convolutional neural networks machine learning.
  It has been proposed organizational and technical solutions to speed up the data processing
  cycle, taking into account the requirements of the legislation regarding the declassification of
  aerial survey data. The results obtained confirm that after the issuance of the Orion UAV by the
  Federal Air Transport Agency of the aircraft type certificate, which gives the right to perform
  commercial flights in the shared airspace of the Russian Federation, it will be possible to implement
  an aerial survey complex of high productivity and degree of autonomy using cut of the edge
  CV & ML technologies. It seems the tactical, technical and economic capabilities of which proposed
  will be orders of magnitude superior to the currently existing solutions especially for hardto-
  reach regions.

• NOISE GENERATION METHOD BASED ON A SET OF NOISY IMAGES WITHOUT CLEAN EXAMPLES

  А.S. Kovalenko , Y. М. Demyanenko

  243-254

  2025-11-10

  Abstract ▼

  In this work, a novel method is proposed for noise generation from noisy images that does not require aligned pairs of clean and noisy data. Unlike traditional approaches demanding matched image sets or a priori noise models, the developed technique models complex noise characteristics intrinsic to specific CMOS sensors solely from observed noisy data. Noise synthesis is achieved via a U‑Net‑like generative adversarial architecture based on StyleGANv2, featuring a modified discriminator conditioned on camera parameters and input image metadata. Special emphasis is placed on preserving the spatial–color structure and textural details of each image, enforced through a dedicated loss function that ensures fidelity to the original color rendering and fine-grained patterns. Training of the noise generator is performed without any paired clean and noisy images, which proves particularly valuable when handling real-world datasets acquired from multiple camera models under varied lighting conditions. The experimental section presents a detailed comparative analysis of the synthesized images using PSNR and SSIM metrics, along with an evaluation of the noise distribution based on intensity statistics and spectral characteristics. It is demonstrated that the generated dataset functions effectively as a standalone training corpus for denoising neural networks and, when combined with a real dataset (e.g., SIDD), yields further enhancements in denoising performance. Results indicate that combined training on the union of generated and real examples produces an average PSNR improvement of 1.5 dB compared to existing methods reliant on aligned data. Independence from the specific optical characteristics of any given sensor significantly broadens the method’s applicability. These findings confirm the utility of the proposed approach for realistic noise synthesis and removal in scenarios lacking clean reference images, and they open avenues for future research into adaptive noise-model generation

• MODERN APPROACHES TO NATURAL FIRE MONITORING AND FORECASTING: REVIEW AND CONCEPT OF AUTONOMOUS UAV-BASED SYSTEM

  N.D. Boldyrev , V. V. Gilka , А.S. Kuznetsova , D.А. Morozov

  58-80

  2025-12-30

  Abstract ▼

  Natural fires cause serious damage to ecosystems, the economy, and public safety every year, and timely detection of fires and prediction of their development increases the speed of response to threats and allows for optimal allocation of resources during emergency response. Existing monitoring methods are limited by the speed of detecting fire outbreaks and the speed of their further spread, which reduces the effectiveness of rescue services. To solve this problem, heterogeneous data sources can be used, including unmanned aerial vehicles (UAVs), distributed sensor networks, mobile field observation systems, ground-based thermal imaging stations, etc., which can contribute to a more accurate analysis of the current situation and improve the reliability of predictive models of fire spread. The aim of the study was to develop a concept for an automated approach to monitoring and predicting wildfires based on unmanned aerial vehicles. We believe that this approach will improve the speed of detecting fire outbreaks and the accuracy of predicting their spread. The tasks include analyzing existing monitoring methods, developing a concept for a system that integrates multispectral imaging, optimized data transmission, automatic segmentation, and forecasting based on machine learning, as well as ensuring interaction between the operator and alert specialists. The work used methods of collecting, analyzing, and transmitting data from UAVs, processing multispectral images, machine learning and neural networks for fire detection, image segmentation algorithms and simulation modeling for fire spread prediction, data visualization to support decision-making by operators and administrators, logging and analysis of results for model training, software engineering, and human-computer interaction technologies. The system will reduce the time required to detect and predict fires, enable operators to launch multiple drones simultaneously, and automate the processing of data received from them. Process automation will reduce emergency response times and staffing levels, improve resource allocation, increase forecast accuracy, and improve the timeliness of emergency service notifications. This will help reduce damage from wildfires and improve the safety of people and ecosystems. Despite the progress made in addressing this challenge, the comprehensive system described in this article does not yet exist in its entirety in Russia, the CIS countries, or in Western and Asian countries. Although individual components, such as UAVs for monitoring and artificial intelligence (AI) for data analysis, are already in active use, there is currently no integrated solution that combines all elements (drone control, near real-time fire spread prediction, data transmission, and interaction with emergency services). does not currently exist. This concept represents a new approach that could become a breakthrough technology for combating natural disasters.