1. ELARTU — Інституційний репозитарій ТНТУ імені Івана Пулюя
  2. Факультет прикладних інформаційних технологій та електроінженерії (ФПТ)
  3. Кафедра біотехнічних систем (БТ)
  4. Наукові публікації працівників кафедри біотехнічних систем
Ezzel az azonosítóval hivatkozhat erre a dokumentumra forrásmegjelölésben vagy hiperhivatkozás esetén: http://elartu.tntu.edu.ua/handle/lib/50816
Összes dokumentumadat
DC mezőÉrtékNyelv
dc.contributor.authorYavorska, Evhenia-
dc.contributor.authorYavorskyy, Bohdan-
dc.contributor.authorHryniuk, Ivan-
dc.contributor.authorTiutiunnyk, Oksana-
dc.contributor.authorPinaiev, Bogdan-
dc.contributor.authorZhukov, Alexey-
dc.contributor.authorDzierżak, Róża-
dc.contributor.authorMarassulov, Ussen-
dc.date.accessioned2025-12-31T18:56:24Z-
dc.date.available2025-12-31T18:56:24Z-
dc.date.issued2025-12-
dc.date.submitted2025-
dc.identifier.citationEvhenia Yavorska, Ivan Hryniuk, Bohdan Yavorskyy, Oksana Tiutiunnyk, Bogdan Pinaiev, Alexey Zhukov, Róża Dzierżak, and Ussen Marassulov "Modern methods of image quality enhancement in intrascopic medical imaging: comparative analysis and development trends", Proc. SPIE 14009, Photonics Applications in Astronomy, Communications, Industry, and High Energy Physics Experiments 2025, 140090X (30 December 2025); https://doi.org/10.1117/12.3099346uk_UA
dc.identifier.urihttp://elartu.tntu.edu.ua/handle/lib/50816-
dc.description.abstractThis article presents a systematic review of modern methods for enhancing image quality in intrascopic diagnostic systems. It discusses the technical aspects of intrascopy, classifies image enhancement algorithms, and analyzes the role of artificial intelligence and deep learning. A comparative analysis of traditional and AI-based methods is provided, along with an outlook on emerging technologies.uk_UA
dc.description.sponsorship[16] Xu, Y., et al., “Retinex-Net: Deep Retinex decomposition for low-light enhancement,” Proc. BMVC (2020). [17] IEEE, [IEEE Standard for Medical Image Quality Metrics], IEEE Standards Association (2022).uk_UA
dc.format.extent1-7-
dc.publisherLublin, Polanduk_UA
dc.relation.urihttps://doi.org/10.1117/12.3099346uk_UA
dc.subjectintrascopyuk_UA
dc.subjectimage processinguk_UA
dc.subjectquality improvementuk_UA
dc.subjectdeep learninguk_UA
dc.subjectneural networksuk_UA
dc.subjectCLAHEuk_UA
dc.subjectBM3Duk_UA
dc.titleModern methods of image quality enhancement in intrascopic medical imaging: comparative analysis and development trendsuk_UA
dc.typeProceedings Bookuk_UA
dc.rights.holderEvhenia Yavorskauk_UA
dc.rights.holderBohdan Yavorskyyuk_UA
dc.rights.holderIvan Hryniukuk_UA
dc.rights.holderOksana Tiutiunnykuk_UA
dc.rights.holderBogdan Pinaievuk_UA
dc.rights.holderAlexey Zhukovuk_UA
dc.rights.holderRóża Dzierżakuk_UA
dc.rights.holderUssen Marassulovuk_UA
dc.coverage.placenameLublin, Polanduk_UA
dc.relation.references1. Kvyetnyy, R., Maslii, R., Harmash, V., Bogach, I., Kotyra, A., Grądz, Ż., Zhanpeisova, A. and Askarova, N., “Object detection in images with low light condition,”Proc. SPIE 10445, Photonics Applications in Astronomy, Communications, Industry, and High Energy Physics Experiments 2017, 104450W (7 August 2017), https://doi.org/10.1117/12.2281001uk_UA
dc.relation.references2. Dubolazov, O. V., Ushenko, A. G., Ushenko, Y. A., Sakhnovskiy, M. Y., et al., “Laser Müller matrix diagnostics of changes in the optical anisotropy of biological tissues,” in [Information Technology in Medical Diagnostics II], 195203 (2019).uk_UA
dc.relation.references3. Franchevska, H., et al., CEUR Workshop Proc. 3468, 263–272, 1st Int. Workshop CITI 2023, Ternopil (2023).uk_UA
dc.relation.references4. Dozorskyi, V., Dozorska, O., Yavorska, E., et al., “The method of detection of speech process signs in the structure of electroencephalographic signals,” CEUR Workshop Proc. 3309, 387–395, 2nd Int. Workshop ITTAP 2022, Ternopil (2022).uk_UA
dc.relation.references5. Romanyuk, O. N., Pavlov, S. V., Romanyuk, O. V., et al., “A function-based approach to realtime visualization using graphics processing units,” Proc. SPIE 11581, 115810E (2020).uk_UA
dc.relation.references6. Litjens, G., et al., “A survey on deep learning in medical image analysis,” Med. Image Anal. (2017). [uk_UA
dc.relation.references7. Vasilevskyi, O., Voznyak, O., Didych, V., Sevastianov, V., Ruchka, O., and Rykun, V., “Methods for constructing high-precision potentiometric measuring instruments of ion activity,” Proc. IEEE 41st Int. Conf. Electronics and Nanotechnology (ELNANO), 247–252 (2022).uk_UA
dc.relation.references8. Zhou, S. K., et al., “A review of deep learning in medical imaging: Imaging traits, technology trends, case studies,” IEEE Trans. Med. Imaging (2021).uk_UA
dc.relation.references9. Vysotska, O. V., Georgiyants, M., et al., “An approach to determination of the criteria of harmony of biological objects,” Proc. SPIE 10808, 108083B (2018)uk_UA
dc.relation.references10. Ronneberger, O., et al., “U-Net: Convolutional networks for biomedical image segmentation,” Proc. MICCAI (2015).uk_UA
dc.relation.references11. Wang, Z., Bovik, A. C., Sheikh, H. R., and Simoncelli, E. P., “Image quality assessment: from error visibility to structural similarity,” IEEE Trans. Image Process. (2004).uk_UA
dc.relation.references12. Wójcik, W., Pavlov, S., and Kalimoldayev, M., [Information Technology in Medical Diagnostics II], Taylor & Francis Group, CRC Press, Balkema Book, London, 336 p. (2019).uk_UA
dc.relation.references13. Khvostivskyi, M., et al., “Mathematical, algorithmic and software support for phonocardiographic signal processing to detect mitral insufficiency of human heart valves,” CEUR Workshop Proc. 3628, 350–357, 3rd Int. Workshop ITTAP 2023, Ternopil (2023).uk_UA
dc.relation.references14. Zhang, K., et al., “Beyond a Gaussian denoiser: Residual learning of deep CNN for image denoising,” IEEE Trans. Image Process. (2017).uk_UA
dc.relation.references15. Dabov, K., et al., “Image denoising by sparse 3D transform-domain collaborative filtering,” IEEE Trans. Image Process. (2007).uk_UA
dc.relation.references16. Xu, Y., et al., “Retinex-Net: Deep Retinex decomposition for low-light enhancement,” Proc. BMVC (2020).uk_UA
dc.relation.references17. IEEE, [IEEE Standard for Medical Image Quality Metrics], IEEE Standards Association (2022).uk_UA
dc.identifier.doihttps://doi.org/10.1117/12.3099346-
dc.contributor.affiliationTernopil Ivan Puluj National Technical University (Ukraine)uk_UA
dc.contributor.affiliationVinnytsia National Technical University (Ukraine)uk_UA
dc.contributor.affiliationLublin University of Technology (Poland)uk_UA
dc.contributor.affiliationAkhmet Yassawi International Kazakh-Turkish University (Kazakhstan)uk_UA
dc.citation.conferenceProc. SPIE 14009, Photonics Applications in Astronomy, Communications, Industry, and High Energy Physics Experiments 2025, 140090X (30 December 2025)-
dc.coverage.countryPLuk_UA
Ebben a gyűjteményben:Наукові публікації працівників кафедри біотехнічних систем

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