Medical Image Denoising: Techniques, Challenges, and Future Directions

Medical image denoising is a crucial step in the processing and analysis of diagnostic images, aiming to remove unwanted noise while preserving essential diagnostic details. Traditional denoising methods, including spatial and transform domain filters, have been widely utilized in medical image denoising. However, these techniques face limitations when dealing with complex noise patterns and maintaining fine image details. In recent years, deep learning-based methods have emerged as a promising approach for medical image denoising. These methods, leveraging deep neural networks such as Convolutional Neural Networks (CNNs), Autoencoders, and Generative Adversarial Networks (GANs), have demonstrated significant potential in learning underlying patterns in images to remove noise while preserving diagnostic information. This paper provides a comprehensive review of the types of noise in medical images and classical denoising methods. We also explore recent advancements in deep learning-based techniques for medical image denoising, alongside the evaluation metrics commonly used to assess their performance. Furthermore, we address the challenges in medical image denoising and propose future research directions in this field. The aim of this paper is to provide an extensive overview of the state-of-the-art techniques in medical image denoising, highlighting the challenges and opportunities for innovation. Additionally, we emphasize emerging techniques, including hybrid models, Graph Neural Networks (GNNs), and self-supervised learning, as well as the potential for further enhancements in medical image quality using advanced denoising approaches.