Vadym Savanevych

This study investigates the process of detecting and determining the position of small-sized moving objects in sequences of images in the short-wave infrared range (SWIR). The task addressed is to enable the detection of low-contrast objects and coordinate accuracy with acceptable speed under conditions of inhomogeneous and time-varying background. A multi-stage pipeline for processing SWIR images has been proposed, based on signal path decomposition into nine consecutive stages. A characteristic feature of the devised solution is the separation of operations of software averaging binning, spatial-temporal background compensation, and matched filtering. This enables end-to-end pixel-by-pixel control over a frame's signal-to-noise characteristics. The results are attributed to the fact that averaging pixel blocks at stage 0 quadratically reduces the amount of data for subsequent operations, and the use of temporal interframe median filtering (TIMF) at stage 2 isolates spatially invariant interference from object movement. It has been experimentally proven that the use of binning factors k = 2 and k = 4 increases the processing speed of frames with a size of 1280 × 1024 pixels from 54.99 to 784.31 frames/s. At the same time, the coordinate error remains in the subpixel range (1.76 pixels) with a probability of correct detection PD = 0.93, and the use of temporal TIMF reduces the average number of false detections to NF = 0.0035 per frame. For matched filtering, a non-monotonic effect of K on PD and coordinate accuracy has been established. These results are due to changes in spatial discretization, background compensation, and matching of the filter to the object scale. Multi-criteria Pareto analysis made it possible to weed out non-dominated variants and form a set of 25 practically rational pipeline configurations for adaptation to the hardware limitations of optoelectronic systems

This study explores processes that measure and evaluate coordinate and photometric characteristics of moving objects in computer vision tasks on digital images and series of frames. The task addressed relates to the lack of a comprehensive formalized method for end-to-end evaluation of the accuracy of coordinate and photometric measurements, which integrates high-fidelity simulation modeling, automatic data identification, and statistical analysis of the laws of error distribution in terms of structurally heterogeneous subsamples. To solve the task, a computational method for evaluating measurement accuracy indicators based on data from the SCVModel simulation modeling system has been devised. The method is based on a formalized representation of observation conditions through a morphological table with the use of cross-consistency testing to cut off physically unrealistic scenarios. An extended set of statistical characteristics has been defined and spatial-energy criteria for grouping measurements into subsamples have been introduced. A distinctive feature of the devised method is that, unlike the generally accepted threshold metrics of integral quality, it enables studying measurement errors. They include full-fledged random variables with the calculation of their mathematical expectations, variances, asymmetry, and kurtosis. The results indicate that the mechanism behind spatial censoring of subsamples effectively isolates anomalous deviations caused by optical distortion or overlapping of objects. It is quantitatively proven that rejecting 5–10% of such measurements leads to a sharp decrease in the mean square deviation (from 6.30 to 0.40 pixels). The scope of practical use of the method is verification and comparative analysis of moving object detectors. This method is implemented in the COMPARATOR software toolkit (Ukraine) and has been successfully applied to evaluate the Amanita (Ukraine) and YOLOv8 (USA) systems

Astronomical data mining plays a crucial role in modern astrophysics, enabling the discovery and analysis of celestial phenomena through the processing of vast observational datasets. As the volume of astronomical data continues to grow exponentially, the need for efficient, automated decision-making within data processing pipelines becomes increasingly critical. This paper presents an artificial intelligence (AI) driven decision-making module designed to optimize workflow management and anomaly detection in large-scale astronomical data processing. Integrated with a PostgreSQL-based logging system, it enhances real-time monitoring, streamlines error identification, and improves overall data processing efficiency. The proposed approach leverages advanced computational techniques to automate key decision points, reducing manual intervention and mitigating the risk of processing failures. We outline the methodology and architectural framework, detailing its implementation and integration into existing data processing pipelines in the Lemur software of the CoLiTec (Collection Light Technology) project. A comparative analysis with conventional techniques highlights the advantages of the proposed system in terms of accuracy, computational efficiency, and robustness. Experimental results demonstrated significant improvements in identifying anomalies, optimizing resource allocation, and enhancing the reliability of automated decision-making process. The proposed hybrid rule-based + AI approach demonstrated a 65% improvement in decision-making speed and a 50% reduction in failure recovery time compared to traditional rule-based monitoring. The findings underscore the potential of AI-driven decision modules in advancing astronomical research by enabling more efficient and accurate data analysis within large-scale observational studies.

In this paper we present the result of the combined use of the OMT-800, AZT-3, KIT telescopes, and the Lemur software of the CoLiTec project. The paper considers in detail several of the astrometric observations of Jupiter Trojan asteroids: (617) Patroclus, (3548) Eurybates, and (21900) Orus from a long list of small bodies of the Solar System.

In this paper the authors presented a sophisticated data mining pipeline, which was designed for restoring the high-quality images from the blurred frames made by the Charge-Coupled Device (CCD) cameras. The developed data mining pipeline leverages the modern informational technologies for the horizontal and vertical scalability. The core methodology integrates the following mathematical methods and algorithms: an inverse median filtration method for the noise reduction and the Lucy-Richardson algorithm for deblurring. The inverse median filtration effectively reduces impulsive noise while preserving edges, and the Lucy-Richardson algorithm iteratively refines the image by correcting for blurring effects encoded in the point spread function (PSF). The proposed system's architecture of the processing pipeline for automated data mining of the single astronomical objects from blurred CCD frames utilizes the following modern technologies: Python programming language, Redis, FastAPI, React, Docker, and Caddy to ensure high performance, scalability, and ease of deployment. This integrated approach significantly enhances the accuracy of astronomical observations, facilitating more precise studies of celestial objects. The proposed pipeline addresses the unique challenges of astronomical image processing, offering a robust solution for automated data mining of single astronomical objects. Our work demonstrates the potential to advance astronomical research by improving image clarity and reliability, contributing to various fields within astronomy. The combination of effective noise reduction and deblurring techniques, along with a scalable and high-performance system architecture, provides a comprehensive solution to the challenges faced in processing astronomical images.

The object of this study is the process of filtering astronomical frames that contain images of potential objects in the Solar System. To recognize the image of each such object in contrast with the background of the frame, it is necessary to carry out frequency filtering of the image. Any frequency filtering using various image filters is aimed at reducing the dynamic range of the background substrate. Also, frequency filtering leads to an increase in the signal-to-noise ratio of the entire image or its fragments, depending on the configuration. However, the identified problem area of each image during frequency filtering is the distortion of the structure of its edges. Therefore, to solve this problem, an edge effect compensation procedure has been proposed to eliminate structural distortions during frequency filtering. Complementing the image with borders on all sides and the augmented extended image made it possible to introduce a formal connection between the pixel values of the extended image fragment and the pixel values of the extended original image. Testing was carried out using a high-pass Gaussian filter. The use of the devised edge effect compensation procedure made it possible to remove distortion of the structure of the image edges. The devised edge effect compensation procedure was tested in practice within the CoLiTec project. It was implemented during the in-frame processing stage of the Lemur software. The study showed that the use of the devised edge effect compensation procedure makes it possible to remove image artifacts compared to conventional filtering without taking into account the edge effect. Also, owing to edge effect compensation, structural image distortions were eliminated, and the signal-to-noise ratio was increased by 7–10 times

The astronomical images are made by cameras with the charge-coupled device (CCD). They can be received from the different sources, like servers, clusters, predefined series, archives or “live” (online) data streams. The astronomical images processing is focused on but not limited to the following tasks: data mining, knowledge discovery, big astronomical data processing, filtering, background alignment, brightness equalization, segmentation, classification, image recognition, object's image detection, object's astrometry and photometry, moving object detection, parameters determination of the object's image and apparent motion, reference objects selection and others. The modern Lemur software of the Collection Light Technology (CoLiTec) project (https://colitec.space) was developed using the described above technologies and approaches. The Lemur software is designed to perform a sequence of the following main steps: pre-processing (astronomical information collection -> worst data rejection -> useful data extraction -> data mining -> classification -> background alignment -> brightness equalization), image processing (segmentation -> typical form analysis -> recognition patterns applying -> detection of the object's image -> astrometry -> photometry -> objects identification -> tracks detection), knowledge discovery (Solar System objects or artificial satellites to be discovered, tracks parameters for the investigation, light curves of the variable stars, scientific reports in the international formats). The paper describes the modern features for the astronomical image processing implemented in the Lemur software. It has assisted in making over 1700 discoveries of asteroids, including 5 NEOs, 21 Trojan asteroids of Jupiter, 1 Centaur. In total it has been used in about 800 000 observations, during which five comets were discovered. All such observations and discoveries have official observational circulares from the Minor Planet Center (MPC) of the International Astronomical Union (IAU).

The object of this study is the process of filtering astronomical frames that contain images of potential objects in the Solar System. To contour the image of each such object and recognize it in contrast with the background of the frame, it is necessary to filter the image. Most often, a variety of high-pass filters are used to determine the high-frequency component of the image, which can be removed as a coarse-grained component. Any image filtering is aimed at increasing the signal-to-noise ratio and reducing the dynamic range of the background image. However, the filtering process is quite resource- and time-consuming. This is especially true for systems for parallel processing of series of astronomical frames in real time (online). Therefore, to solve the problem of lack of frame fragmentation, which leads to high consumption of RAM, a procedure for fragmenting astronomical frames has been proposed. Owing to the introduction of a formal connection between the values of frame pixels and fragments, as well as determining their number, it was possible to reduce RAM utilization. Testing was carried out using the following high-pass filters ‒ ideal filter, Butterworth filter, and Gaussian filter. Using the devised procedure for fragmenting astronomical frames has made it possible to reduce the utilization of RAM during filtering. As a result, with parallel processing, this has also made it possible to speed up the high-frequency filtering procedure itself. The procedure devised for fragmenting astronomical frames was tested in practice within the framework of the CoLiTec project. It was implemented in the On-Line Data Analysis System (OLDAS) of the Lemur software. The study showed that when using the devised procedure, RAM utilization was reduced by 7–10 times. And the speed of filtration itself increased by 2–3 times. Accordingly, the processing time for each astronomical frame was reduced by 2–3 times

The object of this study is the background substrate of astronomical frames. To detect and compare the image of an object in a frame with its real image from astronomical catalogs, it is necessary to uniformly distribute the brightness of the background image substrate. Most often, the background alignment of astronomical frames is performed using the hardware calibration method applying the construction of service frames. However, it does not make it possible to eliminate the background from temporary stray light. Therefore, to solve this problem, a procedure has been proposed for brightness alignment of the background frame using high-pass filtering. For high-pass filtering of images, three high-pass filters were considered – an ideal filter, a Butterworth filter, and a Gaussian filter. To remove coarse-grained image components from the image, a high-pass filter was used, which attenuates low-frequency harmonics of the image spectrum while simultaneously passing high-frequency harmonics. Applying the devised procedure for brightness alignment of the background substrate of the frame has made it possible to increase the signal-to-noise ratio and reduce the dynamic range of the background substrate of the image. The study showed that when assessing brightness and identifying frames, the fitting provides better accuracy of reference to the starry sky. Also, the standard deviation of frame identification errors in this case is 5–7 times less than without using the devised procedure. The devised procedure for brightness alignment of the background frame substrate was tested in practice within the framework of the CoLiTec project. It was implemented at the stage of intra-frame processing in the Lemur software for automated detection of new objects and tracking of known objects