结构光照明超分辨显微镜（SIM）已成为分子细胞生物领域进行活细胞动态过程实时观测的重要工具。SIM作为一种计算成像方法，其成像质量很大程度依赖于超分辨图像重建算法的优劣。近5年来，陆续报道了近10种针对不同条件下的SIM超分辨成像开源算法，基于深度学习的SIM重建算法也层出不穷。理解各算法的原理及异同从而在实际成像实验中选择合适的成像算法，成为了SIM成像技术应用的重要环节。首先介绍了SIM成像的原理；然后分别从结构光参数估计、频谱优化的重建算法、基于深度学习的重建算法三方面介绍了SIM重建算法的最新进展，为SIM研究者及用户提供参考；最后总结了高质量SIM超分辨图像重建仍需解决的问题。

Structured illumination microscopy (SIM) has become one of the most popular super-resolution (SR) instruments for dynamic imaging of live cells. However, the final SR images of SIM depends heavily on the image reconstruction algorithms, which could dramatically affect the image quality. In the past five years, nearly 10 open-source software packages for SIM reconstruction have been developed with advantage on different situations. And deep learning based SIM reconstruction algorithms has also been reported. Understanding the principles and differences of each algorithm becomes a priority to select the appropriate algorithms for practical applications. This review firstly introduces the principle of SIM, and then presents the latest advances for the reference of SIM researchers and users from three aspects: estimation of structured illumination parameter, spectrum optimization, and deep learning based reconstruction. Finally, the remaining issues that need to be addressed further for high-quality SIM super-resolution image reconstruction are summarized.