用物理感知深度学习实现高光谱图像超分辨率与恢复，提升16倍像素精度
Hyperspectral Image Restoration and Super-resolution with Physics-Aware Deep Learning for Biomedical Applications
- 基于成像模型优化的物理感知深度学习方法
- 实现16倍像素超分辨率与12倍成像速度提升
- 适用于生物医学研究，可揭示疾病代谢异常
高光谱成像是一种强大的生物成像工具，因其对物质内在属性的敏感性而能揭示新见解。然而，这种增强对比度以系统复杂性为代价，受限于空间分辨率、光谱分辨率和成像速度之间的固有权衡。为克服此限制，我们提出一种基于深度学习的方法，在不依赖先验知识的情况下，对采集后的图像进行恢复与分辨率提升。通过与成像模型一致的指标微调，该物理感知方法实现了16倍像素超分辨率增强和12倍成像速度提升，且无需额外训练数据进行迁移学习。在五种不同样本类型的合成与实验数据上验证，模型保持了生物完整性，未丢失或虚构特征。我们还明确展示了其揭示唐氏综合征相关代谢变化的能力，这些变化在传统方法下无法检测。此外，我们提供了对模型内部机制的物理解释，为未来可解释性优化铺平道路，有望突破仪器极限。所有方法均开源发布于GitHub。
原文摘要 · Abstract (English)
Hyperspectral imaging is a powerful bioimaging tool which can uncover novel insights, thanks to its sensitivity to the intrinsic properties of materials. However, this enhanced contrast comes at the cost of system complexity, constrained by an inherent trade-off between spatial resolution, spectral resolution, and imaging speed. To overcome this limitation, we present a deep learning-based approach that restores and enhances pixel resolution post-acquisition without any a priori knowledge. Fine-tuned using metrics aligned with the imaging model, our physics-aware method achieves a 16X pixel super-resolution enhancement and a 12X imaging speedup without the need of additional training data for transfer learning. Applied to both synthetic and experimental data from five different sample types, we demonstrate that the model preserves biological integrity, ensuring no features are lost or hallucinated. We also concretely demonstrate the model's ability to reveal disease-associated metabolic changes in Downs syndrome that would otherwise remain undetectable. Furthermore, we provide physical insights into the inner workings of the model, paving the way for future refinements that could potentially surpass instrumental limits in an explainable manner. All methods are available as open-source software on GitHub.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。