Super-resolution (SR) techniques have revolutionized the field of biomedical applications by detailing the structures at resolutions beyond the limits of imaging or measuring tools. These techniques have been applied in various biomedical applications, including microscopy, magnetic resonance imaging (MRI), computed tomography (CT), X-ray, electroencephalogram (EEG), ultrasound, etc. SR methods are categorized into two main types: traditional non-learning-based methods and modern learning-based approaches. In both applications, SR methodologies have been effectively utilized on biomedical images, enhancing the visualization of complex biological structures. Additionally, these methods have been employed on biomedical data, leading to improvements in computational precision and efficiency for biomedical simulations. The use of SR techniques has resulted in more detailed and accurate analyses in diagnostics and research, essential for early disease detection and treatment planning. However, challenges such as computational demands, data interpretation complexities, and the lack of unified high-quality data persist. The article emphasizes these issues, underscoring the need for ongoing development in SR technologies to further improve biomedical research and patient care outcomes.

超分辨率 (SR) 技术以超出成像或测量工具限制的分辨率详细描述结构，彻底改变了生物医学应用领域。这些技术已应用于各种生物医学应用，包括显微镜、磁共振成像 (MRI)、计算机断层扫描 (CT)、X 射线、脑电图 (EEG)、超声等。SR 方法主要分为两种类型：传统非-基于学习的方法和现代基于学习的方法。在这两个应用中，SR 方法已有效地应用于生物医学图像，增强了复杂生物结构的可视化。此外，这些方法已应用于生物医学数据，从而提高了生物医学模拟的计算精度和效率。 SR 技术的使用使得诊断和研究中的分析更加详细和准确，这对于早期疾病检测和治疗计划至关重要。然而，计算需求、数据解释复杂性以及缺乏统一的高质量数据等挑战仍然存在。该文章强调了这些问题，强调需要持续开发 SR 技术，以进一步改善生物医学研究和患者护理结果。