International Research Journal of Innovations in Engineering and Technology - IRJIET International Open Access, Monthly, Peer Reviewed, Reputed Journal ISSN (online): 2581-3048

Impact Factor (2025): 6.9

DOI Prefix: 10.47001/IRJIET

Intelligent Techniques in Image Enhancement: A Review Paper

Ghada M.T. AldabaghMosul University, Mosul, Iraq

Vol 10 No 4 (2026): Volume 10, Issue 4, April 2026 | Pages: 308-314

International Research Journal of Innovations in Engineering and Technology

OPEN ACCESS | Research Article | Published Date: 29-04-2026

doi Logo doi.org/10.47001/IRJIET/2026.104044

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Abstract

Image enhancement has an essential role in improving the quality and interpretability of a digital image, whether it be medical imaging, surveillance, remote sensing, industrial inspection, or multimedia processing applications. Traditional techniques such as histogram equalization, spatial filters, and frequency domain manipulation are complemented (or replaced) by intelligent approaches developed from machine learning, elastic computing, evolutionary optimization, and deep learning. Also, intelligent image enhancement techniques can provide remarkable flexibility, contextual understanding, and resilience from noise, distortion, and variations in illumination.

Artificial neural networks, fuzzy logic, genetic algorithms, swarm intelligence, reinforcement learning, and modern deep learning frameworks are a few examples of intelligent image enhancement techniques studied in this paper. This paper discusses intelligent image enhancement approaches, their benefits, drawbacks, applications, and potential future research areas. Finally, the paper concludes with the primary challenges and opportunities to develop intelligent image enhancement systems.

Keywords

Image Enhancement, Intelligent Image Processing, Digital Image Processing, Machine Learning in Image Enhancement, Deep Learning for Image Enhancement, Artificial Neural Networks (ANN), Fuzzy Logic, Genetic Algorithms, Swarm Intelligence, Reinforcement Learning


Citation of this Article

Ghada M.T. Aldabagh. (2026). Intelligent Techniques in Image Enhancement: A Review Paper. International Research Journal of Innovations in Engineering and Technology - IRJIET, 10(4), 308-314. Article DOI https://doi.org/10.47001/IRJIET/2026.104044

Licence Copyright (c) 2026 International Research Journal of Innovations in Engineering and Technology creative common licence This work is licensed under Creative common Attribution Non Commercial 4.0 Internation Licence
References
  1. Abed, A., & Al-Jawhar, R. (2025). New trends in image restoration using AI models: An analytical study. jport.co.
  2. Ahmed, H. M., & Al-Qattan, Z. M. (2024). Texture-based image enhancement using Gabor filters and morphological processes. Brodatz Texture Dataset, VisTex Dataset, USC Texture Dataset, and Custom grayscale texture images.
  3. Kai, Y., Bian, H., Lin, J., Wang, H., Timofti, R., & Zhang, Y. (2023). Retinexformer: A Retinex-based single-stage transformer for low-light image enhancement. CVF. https://doi.org/10.1109/ICCV51070.2023.01149
  4. Kai, Y., et al. (2024). Retinexformer+: A Retinex-based dual-channel transformer for image enhancement in low-light conditions. ScienceDirect. https://doi.org/10.32604/cmc.2024.057662
  5. Mustafa, T., & Al-Naami, J. S. (2022). Medical image classification using artificial intelligence. Chest X-ray Dataset, MRI Brain Tumor Dataset, NIH ChestX-ray14, Kaggle Medical Imaging Datasets.+
  6. Ni, C., Yang, W., Wang, S., Ma, L., &Kwong, S. (2020). Towards undistinguished deep image enhancement using competitive generative adversarial networks (UEGAN). arXiv. https://arxiv.org/abs/2005.08697
  7. Rees, M. J. C. S. (2025). Image enhancement in low light conditions using deep learning: A lightweight network. MDPI.
  8. Shi, X., et al. (2025). A review of ultra-high-resolution image reconstruction methods using deep learning. Journal of Computer Engineering and Intelligent Methods.
  9. Song, Y., Qian, H., & Du, S. (2021). StarEnhancer: Learning to enhance images in real time with style consideration. arXiv. https://arxiv.org/abs/2102.07512
  10. Wang, L., Zhao, L., Chung, T., & Wu, C. (2024). Image enhancement in low light conditions using competitive generative adversarial networks. Nature.
  11. Yi, S., Shu, H., Zhang, H., Tang, L., & Ma, J. (2023). Diff-Retinex: Rethinking low-light image enhancement using a generative diffusion model. arXiv. https://arxiv.org/abs/2303.XXXXX
  12. Zangana, M., & Mohammad, R. (2025). Image enhancement using deep learning: Techniques and applications. eltikom.poliban.ac.id.
  13. Various Authors. (2025). Review of advances in low-light image enhancement using deep learning. ScienceDirect.
  14. Multiple Authors. (2024). Retinex-based low-light image enhancement network (DEANet++). Springer Link.
  15. Pourasad, Y., Cavallaro, F.: A novel image processing approach to enhancement and compression of X-ray images. International Journal of Environmental Research and Public Health, 18(13), 6724 (2021). https://doi.org/10.3390/ijerph18136724
  16. Xu, Y., Liu, X., Cao, X., Cai, Z., Wang, F., Zhang, J.: Artificial intelligence: A powerful paradigm for scientific research. The Innovation, 2(4), 100179 (2021). https://doi.org/10.1016/j.xinn.2021.100179
  17. Gesnot, R.: The impact of artificial intelligence on human thought. arXiv preprint (2025). https://doi.org/10.48550/arXiv.2508.16628
  18. Razzaq, K., Shah, M.: Machine learning and deep learning paradigms: From techniques to practical applications and research frontiers. Computers, 14(3), 93 (2025). https://doi.org/10.3390/computers14030093
  19. Oulmalme, C., Nakouri, H., Jaafar, F.: A systematic review of generative AI approaches for medical image enhancement: Comparing GANs, transformers, and diffusion models. International Journal of Medical Informatics, 199, 105903 (2025). https://doi.org/10.1016/j.ijmedinf.2025.105903
  20. Schaefferkoetter, J., Yan, J., Ortega, C., Sertic, A., Lechtman, E., Eshet, Y., Metser, U., & Veit-Haibach, P. (2020). Convolutional neural networks for improving image quality with noisy PET data. EJNMMI Research, 10, 105. https://doi.org/10.1186/s13550-020-00695-1
  21. Wang, W. (2021). An improved denoising model for convolutional neural network. Journal of Physics: Conference Series, 1982(1), 012169. IOP Publishing. https://doi.org/10.1088/1742-6596/1982/1/012169
  22. Kajo, I., Kas, M., Chahi, A., & Ruichek, Y. (2023). Learning by competing: Competitive multi-generator based adversarial learning. Applied Soft Computing, 146, 110698. https://doi.org/10.1016/j.asoc.2023.110698
  23. Purwono, Wulandari, A. N. E., Ma’arif, A., & Salah, W. A. (2025). Understanding generative adversarial networks (GANs): A review. Control Systems and Optimization Letters, 3(1), 36. https://doi.org/10.59247/csol.v3i1.170
  24. Tang, H. H., & Ahmad, N. S. (2024). Fuzzy logic approach for controlling uncertain and nonlinear systems: A comprehensive review of applications and advances. Systems Science & Control Engineering, 12(1), 2394429. https://doi.org/10.1080/21642583.2024.2394429
  25. Hooda, D. S., & Raich, V. (2017). Fuzzy logic models and fuzzy control: An introduction. Alpha Science International Ltd.
  26. Reddy, M. J., & Kumar, D. N. (2020). Evolutionary algorithms, swarm intelligence methods, and their applications in water resources engineering: A state-of-the-art review. H2Open Journal, 3(1), 135–188. https://doi.org/10.2166/h2oj.2020.128
  27. M. Almufti, S., Ahmad Shaban, A., Ismael Ali, R., & A. Dela Fuente, J. (2023). Overview of Metaheuristic Algorithms. Polaris Global Journal of Scholarly Research and Trends, 2(2), 10–32. https://doi.org/10.58429/pgjsrt.v2n2a144
  28. Mirzaei, A., & Aghsami, A. (2025). A hybrid deep reinforcement learning architecture for optimizing concrete mix design through precision strength prediction. Mathematical and Computational Applications, 30(4), 83. https://doi.org/10.3390/mca30040083
  29. Park, J., Lee, J.-Y., Yoo, D., & Kweon, I. S. (2018). Distort-and-recover: Color enhancement using deep reinforcement learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/CVPR.2018.00621

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