用光场感知提升深度学习显微重建，减少伪影，增强泛化能力。
Enhancing Deep Learning Based Structured Illumination Microscopy Reconstruction with Light Field Awareness
- 直接估计实际光场，纠正分布偏移带来的误差
- 在模拟与活细胞数据上降低7%的归一化均方根误差
- 适合复杂生物系统中需要高精度重建的场景
结构光照明显微镜（SIM）是动态活细胞亚细胞成像的关键技术。传统SIM重建依赖于照明模式的精确估计，若估计不准则易引入伪影。尽管基于深度学习的方法提升了速度、准确性和鲁棒性，但在分布外数据上仍表现不佳。为此，我们提出光场感知的SIM（AL-SIM）重建方法，直接估计实际光场以纠正因数据分布偏移导致的误差。在模拟纤丝结构和活体BSC1细胞上的综合实验表明，该方法将归一化均方根误差（NRMSE）降低7%，显著减少重建伪影。通过抑制伪影并提升整体精度，AL-SIM扩展了SIM在复杂生物系统中的应用潜力。
原文摘要 · Abstract (English)
Structured illumination microscopy (SIM) is a pivotal technique for dynamic subcellular imaging in live cells. Conventional SIM reconstruction algorithms depend on accurately estimating the illumination pattern and can introduce artefacts when this estimation is imprecise. Although recent deep learning-based SIM reconstruction methods have improved speed, accuracy, and robustness, they often struggle with out-of-distribution data. To address this limitation, we propose an Awareness-of-Light-field SIM (AL-SIM) reconstruction approach that directly estimates the actual light field to correct for errors arising from data distribution shifts. Through comprehensive experiments on both simulated filament structures and live BSC1 cells, our method demonstrates a 7% reduction in the normalized root mean square error (NRMSE) and substantially lowers reconstruction artefacts. By minimizing these artefacts and improving overall accuracy, AL-SIM broadens the applicability of SIM for complex biological systems.
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