Adane Letta (PhD)Fikir Awoke2026-02-142026-02-142025-10https://etd.aau.edu.et/handle/123456789/7653Medical image denoising is the process of reducing unwanted noise from medical images like X-rays, MRIs, and CT scans to improve diagnostic accuracy and clarity. Accurate diagnosis in medical imaging, particularly in radiology, heavily depends on the clarity and quality of visual data. In X-ray imaging, the presence of noise can obscure critical anatomical details, potentially leading to misinterpretation or delayed diagnosis. While previous methods such as BM3D, DnCNN, and domain-specific architectures like X-ReCNN and X-BDCNN have shown significant performance on denoising tasks, they often rely on predefined noise assumptions or lack mechanisms to adaptively attend to varying noise patterns in different image regions. To address these limitations, we propose an attention-guided dual-path deep neural network designed for blind image denoising of real-world medical X-ray images. Unlike standard attention modules, we integrate spatial and channel noise-aware attention mechanisms for medical X-ray denoising, enabling the network to dynamically focus on important features while effectively distinguishing structural details from noise. Our architecture combines U-Net for capturing detailed spatial features and Dilated CNN for extracting broader contextual information. We train our model on the ChestX-ray8 dataset, where it achieves a performance with an SNR of 37.23, PSNR of 42.08, and SSIM of 0.9736. These results demonstrate the model’s effectiveness in denoising X-ray images while preserving structural integrity. The main contributions include the introduction of a noise-aware attention mechanism and a multi scale dualbranch architecture for complementary feature learning. Nevertheless, the model has limitations, generalizing to other imaging modalities like MRI or CT.en-USAttention mechanismBlind imageCNNMedical image denoisingXray.Thesis