DeepFusion: A Novel Deep Learning Technique for Enhanced Image Super-Resolution

Ultrasound imaging is widely used in medical diagnostics due to its real-time capabilities, non-invasive nature, and cost-effectiveness. However, the inherent low resolution of ultrasound images limits the visibility of fine anatomical details, often leading to diagnostic challenges. To address this problem, we propose a DL based approach for image Super-Resolution that enhances the quality of ultrasound images. Our method leverages CNNs i.e. VGG16 and SRGANs to learn the mapping between LR and HR ultrasound images.We employ an enhanced SRGAN with VGG16 approach, using pairs of down sampled and ultrasound images to train the network. Experimental results on clinical ultrasound datasets demonstrate significant improvements in image quality, as evidenced by quantitative metrics such as PSNR. Visual assessment by medical experts confirms that the super-resolved images maintain critical anatomical features, making them suitable for clinical applications. Our deep learning-based method provides a promising solution for improving ultrasound image resolution, has the ability to improve medical imaging diagnostic accuracy.