Medical Image Enhancement Using Super Resolution Methods

AI-Driven Advances in Low-Dose Imaging and Enhancement-A Review

The widespread use of medical imaging techniques such as X-rays and computed tomography (CT) has raised significant concerns regarding ionizing radiation exposure, particularly among vulnerable populations requiring frequent imaging. Achieving a balance between high-quality diagnostic imaging and minimizing radiation exposure remains a fundamental challenge in radiology. Artificial intelligence (AI) has emerged as a transformative solution, enabling low-dose imaging protocols that enhance image quality while significantly reducing radiation doses. This review explores the role of AI-assisted low-dose imaging, particularly in CT, X-ray, and magnetic resonance imaging (MRI), highlighting advancements in deep learning models, convolutional neural networks (CNNs), and other AI-based approaches. These technologies have demonstrated substantial improvements in noise reduction, artifact removal, and real-time optimization of imaging parameters, thereby enhancing diagnostic accuracy while mitigating radiation risks. Additionally, AI has contributed to improved radiology workflow efficiency and cost reduction by minimizing the need for repeat scans. The review also discusses emerging directions in AI-driven medical imaging, including hybrid AI systems that integrate post-processing with real-time data acquisition, personalized imaging protocols tailored to patient characteristics, and the expansion of AI applications to fluoroscopy and positron emission tomography (PET). However, challenges such as model generalizability, regulatory constraints, ethical considerations, and computational requirements must be addressed to facilitate broader clinical adoption. AI-driven low-dose imaging has the potential to revolutionize radiology by enhancing patient safety, optimizing imaging quality, and improving healthcare efficiency, paving the way for a more advanced and sustainable future in medical imaging.

Spectral band selection and ANIMR-GAN for high-performance multispectral coal gangue classification

Low-energy and efficient coal gangue sorting is crucial for environmental protection. Multispectral imaging (MSI) has emerged as a promising technology in this domain. This work addresses the challenge of low resolution and poor recognition performance in underground MSI equipment. We propose an attention-based multi-level residual network (ANIMR) within a super-resolution reconstruction model (ANIMR-GAN) inspired by CycleGAN. This model incorporates improvements to the discriminator and loss function. We trained the model on 600 coal and gangue MSI samples and validated it on an independent set of 120 samples. The ANIMR-GAN, combined with a random forest classifier, achieved a maximum accuracy of 97.78% and an average accuracy of 93.72%. Furthermore, the study identifies the 959.37 nm band as optimal for coal and gangue classification. Compared to existing super-resolution methods, ANIMR-GAN offers advantages, paving the way for intelligent and efficient coal gangue sorting, ultimately promoting advancements in sustainable mineral processing.