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.

本研究解决了结构光显微镜重建中传统算法对光照模式估计不准确导致人工伪影的问题。提出的光场感知结构光显微镜重建方法（AL-SIM）通过直接估计实际光场来修正数据分布变化带来的错误。实验结果显示，该方法在减少重建伪影和提高精确度方面具有显著效果，促进了该技术在复杂生物系统中的应用。

基于光场感知的深度学习结构光显微镜重建增强