Abstract
Computed tomography (CT) is used to visualize body structures and diagnose anomalies, making it an important tool in medical diagnosis and therapy planning. However, imaging techniques such as CT, MRI, ultrasound (US), and PET are frequently hampered by numerous types of noise, including Gaussian, speckle, Poisson variability, and salt-andpepper disturbances. These noises are created by technological interference, image processing flaws, and patient movement, which reduce image clarity and conceal key diagnostic details. The major difficulty in medical imaging is to remove noise while retaining important diagnostic information. Traditional denoising algorithms, such as Gaussian, median, and Wiener filters, frequently fail to adequately control complicated noise patterns or preserve small image details, limiting their utility in medical applications. This study presents an advanced unsupervised blind image denoising strategy that use an integrated model to treat numerous noise types without requiring paired noisy and clean images. The suggested method uses a deep and dense generative adversarial network (DD-GAN) with a new loss function to efficiently reduce noise and degradation at various intensity levels. This method advances CT image denoising by tackling issues such as intra-class variability, artefact importance, and training complexity, hence enhancing diagnostic reliability and accuracy.
Keywords: Computed Tomography (CT), Deep Dense Generative Adversarial Network (DD-GAN), Deep Learning, Denoising, Gaussian Noise, Salt-Pepper Noise.