DESIGN OF AN INFORMATION SYSTEM FOR RASTER IMAGE PROCESSING
DOI:
https://doi.org/10.20998/2079-0023.2026.01.08Keywords:
information system, design, raster image, functional modeling, model, algorithm, decompositionAbstract
This paper addresses the problem of designing an information system for automated processing of raster images. The need to improve processing efficiency under conditions of increasing volumes of visual data and stricter quality requirements is substantiated. The main image processing operations are identified, including image import, color correction, noise and defect removal, scaling, and cropping. The limitations of existing software tools, which are mainly oriented toward manual or semi-automatic editing, are described. An approach to the development of the information system based on functional modeling is proposed. The structuring of the image processing workflow is performed using the IDEF0 notation, which enables the formalization of relationships between input data, control parameters, mechanisms, and outputs. A context diagram is constructed and decomposed, resulting in the identification of key subprocesses. Information flows between subprocesses and transition conditions between processing stages are defined. An algorithm describing the system operation is developed in the form of pseudocode, covering the full image processing cycle. The algorithm includes file format and integrity verification, metadata analysis, parameter normalization, iterative quality adjustment, defect detection and correction, as well as scaling and cropping. An early rejection mechanism for invalid data is implemented to reduce computational costs. An adaptive correction loop is proposed to achieve target quality metrics without a fixed number of iterations. Operation logging is incorporated to enable monitoring and traceability of processing results. The practical significance of the proposed system lies in its applicability for automated raster image processing across various domains. Its implementation improves processing efficiency, ensures consistency of quality parameters, and reduces the need for manual intervention. The limitations related to the iterative nature of the algorithm and restricted format support are identified. Directions for future research include the integration of machine learning methods and the extension of system functionality.
References
Reis M. J. C. S. Developments of computer vision and image processing: methodologies and applications. Future Internet. 2023, vol. 15, art. 233. DOI: https://doi.org/10.3390/fi15070233.
Hrabovskyi Ye. M., Olenych M. M. Tekhnolohichni protsesy vydavnycho-polihrafichnoi spravy : navchalnyi posibnyk [Technological processes of publishing and printing : study guide]. Kharkiv, KhNEU im. S. Kuznetsia [Simon Kuznets Kharkiv National University of Economics], 2015, 192 p. (In Ukr.).
Chen Y., Sherren K., Smit M., Lee K. Y. Using social media images as data in social science research. New Media & Society. 2023, vol. 25, no. 4, pp. 849–871. DOI: https://doi.org/10.1177/14614448211038761.
Wang L., Song T., Song H.-N., Zhang S. Research on design pattern detection method based on UML model with extended image information and deep learning. Applied Sciences. 2022, vol. 12, art. 8718. DOI: https://doi.org/10.3390/app12178718.
Christidou V., Bonoti F., Hatzinikita V. Visual images in science education. Frontiers in Education. 2023, vol. 8, art. 1181754. DOI: https://doi.org/10.3389/feduc.2023.1181754.
Kassimi S., Moussa H., Sabiki H. Enhancing image denoising: A novel non-local anisotropic diffusion framework based on Caputo derivatives and Gaussian convolution for the Perona–Malik model. Signal Processing. 2024, vol. 222, art. 109521. DOI: https://doi.org/10.1016/j.sigpro.2024.109521.
Lemenkova P. Automation of image processing through ML algorithms of GRASS GIS using embedded Scikit-Learn library of Python. Examples and Counterexamples. 2025, vol. 7, art. 100180. DOI: https://doi.org/10.1016/j.exco.2025.100180.
Piskozub Y., Kudriashova A., Oliyarnyk T. Model faktoriv vplyvu na yakist tsyfrovykh zobrazhen [Model of factors influencing the quality of digital images]. Vymiriuvalna ta obchysliuvalna tekhnika v tekhnolohichnykh protsesakh [Measuring and computing devices in technological processes]. 2024, no. 2, pp. 107–111. DOI: https://doi.org/10.31891/2219-9365-2024-78-12. (In Ukr.).
Xie M., Guo H. Self-adaptive alternating direction method of multipliers for image denoising. Applied Sciences. 2024, vol. 14, no. 22, art. 10427. DOI: https://doi.org/10.3390/app142210427.
Zhang J., Chen C., Chen K., Ju M., Zhang D. Local adaptive image filtering based on recursive dilation segmentation. Sensors. 2023, vol. 23, art. 5776. DOI: https://doi.org/10.3390/s23135776.
Schwarz F., Schwarz K. D., Aranda D. A., Bollens K., Shivananjappa N., Creutzburg R., Dimitrova V. Zero-shot social media crisis classification: A training-free multimodal approach. Applied Sciences. 2026, vol. 16, art. 2192. DOI: https://doi.org/10.3390/app16052192.
Trigka M., Dritsas E. A comprehensive survey of deep learning approaches in image processing. Sensors. 2025, vol. 25, art. 531. DOI: https://doi.org/10.3390/s25020531.
Niu S., Zhang J. Image processing based on convolution neural network. Electronics. 2025, vol. 14, art. 4649. DOI: https://doi.org/10.3390/electronics14234649.
Wang T.-S., Kim G.-T., Shin J., Jang S.-W. Hierarchical image quality improvement based on illumination, resolution, and noise factors for improving object detection. Electronics. 2024, vol. 13, art. 4438. DOI: https://doi.org/10.3390/electronics13224438.
Zhu Y., Guo Y., Gao X., Chen Q., Chen Y., Xiang R., Lin B., Wang L., Lu Y., Yan W. Rapid acquisition of high-pixel fluorescence lifetime images of living cells via image reconstruction based on edge-preserving interpolation. Biosensors. 2025, vol. 15, art. 43. DOI: https://doi.org/10.3390/bios15010043.
Li J., Dou K., Liu J., Li Q., Zhou Y. Research and application of functional model construction method for production equipment operation management and control oriented to diversified and personalized scenarios. Machines. 2026, vol. 14, art. 368. DOI: https://doi.org/10.3390/machines14040368.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
