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http://elartu.tntu.edu.ua/handle/lib/50816| Title: | Modern methods of image quality enhancement in intrascopic medical imaging: comparative analysis and development trends |
| Authors: | Yavorska, Evhenia Yavorskyy, Bohdan Hryniuk, Ivan Tiutiunnyk, Oksana Pinaiev, Bogdan Zhukov, Alexey Dzierżak, Róża Marassulov, Ussen |
| Affiliation: | Ternopil Ivan Puluj National Technical University (Ukraine) Vinnytsia National Technical University (Ukraine) Lublin University of Technology (Poland) Akhmet Yassawi International Kazakh-Turkish University (Kazakhstan) |
| Bibliographic description (Ukraine): | Evhenia 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/10.1117/12.3099346 |
| Conference/Event: | Proc. SPIE 14009, Photonics Applications in Astronomy, Communications, Industry, and High Energy Physics Experiments 2025, 140090X (30 December 2025) |
| Issue Date: | Dec-2025 |
| Submitted date: | 2025 |
| Date of entry: | 31-Dec-2025 |
| Publisher: | Lublin, Poland |
| Country (code): | PL |
| Place of the edition/event: | Lublin, Poland |
| DOI: | https://doi.org/10.1117/12.3099346 |
| Keywords: | intrascopy image processing quality improvement deep learning neural networks CLAHE BM3D |
| Page range: | 1-7 |
| Abstract: | This 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. |
| 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). |
| URI: | http://elartu.tntu.edu.ua/handle/lib/50816 |
| Copyright owner: | Evhenia Yavorska Bohdan Yavorskyy Ivan Hryniuk Oksana Tiutiunnyk Bogdan Pinaiev Alexey Zhukov Róża Dzierżak Ussen Marassulov |
| URL for reference material: | https://doi.org/10.1117/12.3099346 |
| References (Ukraine): | 1. 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/10.1117/12.2281001 2. 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). 3. Franchevska, H., et al., CEUR Workshop Proc. 3468, 263–272, 1st Int. Workshop CITI 2023, Ternopil (2023). 4. 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). 5. 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). 6. Litjens, G., et al., “A survey on deep learning in medical image analysis,” Med. Image Anal. (2017). [ 7. 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). 8. Zhou, S. K., et al., “A review of deep learning in medical imaging: Imaging traits, technology trends, case studies,” IEEE Trans. Med. Imaging (2021). 9. Vysotska, O. V., Georgiyants, M., et al., “An approach to determination of the criteria of harmony of biological objects,” Proc. SPIE 10808, 108083B (2018) 10. Ronneberger, O., et al., “U-Net: Convolutional networks for biomedical image segmentation,” Proc. MICCAI (2015). 11. 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). 12. Wójcik, W., Pavlov, S., and Kalimoldayev, M., [Information Technology in Medical Diagnostics II], Taylor & Francis Group, CRC Press, Balkema Book, London, 336 p. (2019). 13. 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). 14. Zhang, K., et al., “Beyond a Gaussian denoiser: Residual learning of deep CNN for image denoising,” IEEE Trans. Image Process. (2017). 15. Dabov, K., et al., “Image denoising by sparse 3D transform-domain collaborative filtering,” IEEE Trans. Image Process. (2007). 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). |
| Content type: | Proceedings Book |
| Appears in Collections: | Наукові публікації працівників кафедри біотехнічних систем |
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| File | Description | Size | Format | |
|---|---|---|---|---|
| 140090X.pdf | 391,25 kB | Adobe PDF | View/Open |
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