Bridging PCI and CABG in Complex Coronary Disease

Abstract: This paper presents a novel approach for enhancing image resolution using deep learning techniques. By leveraging convolutional neural networks (CNNs), we propose a model that effectively reconstructs high-resolution images from low-resolution inputs. The proposed method incorporates residual learning and multi-scale feature extraction to improve both visual quality and structural fidelity. Experimental results demonstrate significant improvements in peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) compared to existing methods. The model is also computationally efficient, making it suitable for real-time applications. Keywords: super-resolution

convolutional neural networks

residual learning

multi-scale features

image reconstruction