A Review of Artificial Intelligence Techniques for Medical Image Enhancement

Noor K. Younis, Mahmood Hameed Qahtan, Marwa Riyadh Ahmed
International Journal of Computational and Electronic Aspects in Engineering
Volume 6: Issue 2, June 2025, pp 98-107

Author's Information
Noor K. Younis 1 
Corresponding Author
1Artificial Intelligence Techniques Engineering, Northern Technical University, Mosul, Iraq
noorky@ntu.edu.iq

Mahmood Hameed Qahtan2
2Artificial Intelligence Techniques Engineering, Northern Technical University, Mosul, Iraq

Marwa Riyadh Ahmed3
3Artificial Intelligence Techniques Engineering, Northern Technical University, Mosul, Iraq
Review Paper -- Peer Review
First online on – 15 June 2025

Open Access article under Creative Commons License

Cite this article –Noor K. Younis, Mahmood Hameed Qahtan, Marwa Riyadh Ahmed, “A Review of Artificial Intelligence Techniques for Medical Image Enhancement ”, International Journal of Computational and Electronic Aspects in Engineering, RAME Publishers, vol. 6, Issue 2, pp. 98-107, 2025.
https://doi.org/10.26706/ijceae.6.2.20250406

Abstract:-
Medical imaging plays a crucial role in diagnosis, treatment planning, and monitoring of diseases. However, ‎‎the quality of medical images is often compromised due to noise, low resolution, and artifacts. Recent ‎‎advancements in Artificial Intelligence (AI), particularly deep learning techniques, have significantly ‎‎improved image enhancement capabilities in the medical domain. This paper comprehensively reviews AI-‎based image enhancement methods applied to medical imaging. We discuss ‎various enhancement ‎techniques, including denoising, super-resolution, contrast enhancement, and artifact ‎removal. Additionally, ‎we provide an overview of commonly used datasets, evaluation metrics, and recent ‎developments in AI ‎models such as convolutional neural networks (CNNs), generative adversarial networks ‎‎(GANs), and ‎transformer-based architectures. Finally, we highlight current challenges.
Index Terms:-
Medical Imaging, Image Enhancement, Artificial Intelligence, Deep Learning, CNN, GAN
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