Coronary artery disease (CAD) remains a leading global cause of mortality, with X-ray coronary angiography serving as the clinical gold standard for evaluating coronary morphology and stenosis. However, angiographic images are often compromised by low contrast, noise, motion blur, and compression artifacts that obscure fine vascular structures and impede both diagnostic interpretation and automated analysis. To address these limitations, this study proposes a hybrid deep learning framework integrating convolutional neural networks (CNNs) with Transformer-based self-attention mechanisms for coronary angiography enhancement. The CNN component targets local feature extraction to suppress noise,... More >