Deep learning (DL) has become an indispensable tool in hyperspectral data analysis, automatically extracting valuable features from complex, high-dimensional datasets. Super-resolution reconstruction, an essential aspect of hyperspectral data, involves enhancing spatial resolution, particularly relevant to low-resolution hyperspectral data. Yet, the pursuit of super-resolution in hyperspectral analysis is fraught with challenges, including acquiring ground truth high-resolution data for training, generalization, and scalability. The pressing issue of extended spectral acquisition times, notably for high-resolution scans, is a significant roadblock in hyperspectral imaging. Super-resolution methods offer a promising solution by providing higher spatial resolution data to expedite data collection and yield more efficient outcomes. This paper delves into a practical application of these concepts using Raman imaging, where spectral acquisition times can be prohibitively long. In this context, DL-based super-resolution models demonstrate their efficacy by predicting and reconstructing high-resolution Raman data from low-resolution input, eliminating the need for resource-intensive high-resolution scans. While previous work often relied on substantial high-resolution datasets, this study showcases the ability to achieve similar outcomes even with limited data, presenting a more practical and cost-effective approach. The results offer a glimpse into the transformative potential of this technology to streamline hyperspectral imaging applications by saving valuable time and resources through the successful generation of high-resolution data from low-resolution inputs.

中文翻译：

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深度学习驱动的超分辨率拉曼光谱成像：从低分辨率数据进行高效的高分辨率重建


深度学习 （DL） 已成为高光谱数据分析中不可或缺的工具，可自动从复杂的高维数据集中提取有价值的特征。超分辨率重建是高光谱数据的一个重要方面，涉及提高空间分辨率，特别是与低分辨率高光谱数据相关。然而，在高光谱分析中追求超分辨率充满了挑战，包括获取地面实况高分辨率数据以进行训练、泛化和可扩展性。延长光谱采集时间的紧迫问题，特别是对于高分辨率扫描，是高光谱成像的一个重大障碍。超分辨率方法通过提供更高的空间分辨率数据来加快数据收集并产生更高效的结果，从而提供了一种有前途的解决方案。本文深入探讨了使用拉曼成像的这些概念的实际应用，其中光谱采集时间可能非常长。在这种情况下，基于 DL 的超分辨率模型通过从低分辨率输入预测和重建高分辨率拉曼数据来证明其有效性，从而消除了对资源密集型高分辨率扫描的需求。虽然以前的工作通常依赖于大量的高分辨率数据集，但这项研究展示了即使数据有限也能实现类似结果的能力，提出了一种更实用和更具成本效益的方法。结果让我们得以一窥这项技术的变革潜力，通过从低分辨率输入成功生成高分辨率数据来节省宝贵的时间和资源，从而简化高光谱成像应用。