Innovative Approaches to CT Image Enhancement: Deep Learning Solutions for Denoising and Deblurring

Computed Tomography (CT) is essential in medical diagnostics, providing detailed images of internal structures. Noise and blur introduced during image acquisition can com-promise diagnostic accuracy, especially in low-dose CT (LDCT) scans aimed at reducing radiation exposure. Deep learning methods have emerged as effective solutions for addressing these challenges, with significant advancements in both denoising and deblurring techniques. This paper reviews recent deep learning approaches for CT image enhancement, covering supervised and self-supervised methods. The paper also explores emerging trends such as diffusion models and transformer-based architectures that offer improved image quality and restoration efficiency. Simultaneous denoising and deblurring techniques are examined for their potential in clinical applications. Promising future directions include the use of large-scale pretrained models, zero-shot learning, and prompt-based frameworks, which have the potential to further enhance CT image quality while maintaining low radiation exposure, thereby advancing diagnostic capabilities.