Imaging three-dimensional, subcellular structures with high axial resolution has always been the core purpose of fluorescence microscopy. However, trade-offs exist between axial resolution and other important technical indicators, such as temporal resolution, optical power density, and imaging process complexity. We report a new imaging modality, fluorescence interference structured illumination microscopy (FI-SIM), which is based on three-dimensional structured illumination microscopy for wide-field lateral imaging and fluorescence interference for axial reconstruction. FI-SIM can acquire images quickly within the order of hundreds of milliseconds and exhibit even 30 nm axial resolution in half the wavelength depth range without z-axis scanning. Moreover, the relatively low laser power density relaxes the requirements for dyes and enables a wide range of applications for observing fixed and live subcellular structures.

Various super-resolution microscopy techniques have been presented to explore fine structures of biological specimens. However, the super-resolution capability is often achieved at the expense of reducing imaging speed by either point scanning or multiframe computation. The contradiction between spatial resolution and imaging speed seriously hampers the observation of high-speed dynamics of fine structures. To overcome this contradiction, here we propose and demonstrate a temporal compressive super-resolution microscopy (TCSRM) technique. This technique is to merge an enhanced temporal compressive microscopy and a deep-learning-based super-resolution image reconstruction, where the enhanced temporal compressive microscopy is utilized to improve the imaging speed, and the deep-learning-based super-resolution image reconstruction is used to realize the resolution enhancement. The high-speed super-resolution imaging ability of TCSRM with a frame rate of 1200 frames per second (fps) and spatial resolution of 100 nm is experimentally demonstrated by capturing the flowing fluorescent beads in microfluidic chip. Given the outstanding imaging performance with high-speed super-resolution, TCSRM provides a desired tool for the studies of high-speed dynamical behaviors in fine structures, especially in the biomedical field.

荧光显微镜具有对样品损伤小、可特异性成像等优点，是生物医学研究的主流成像手段。随着人工智能技术的快速发展，深度学习在逆问题求解中取得了巨大成功，被广泛应用于诸多领域。近年来，深度学习在荧光显微成像中的应用掀起了一个研究热潮，为荧光显微技术发展提供了性能上的突破与新思路。基于此，首先介绍了深度学习的基本网络模型，然后对基于深度学习的荧光显微成像技术在荧光显微的空间分辨率、图像采集及重建速度、成像通量和成像质量提升方面的应用进行阐述。最后，对目前深度学习在荧光显微成像中的研究进行总结与展望。

观察细胞器间动态相互作用，深入分析作用规律，对于揭示生理病理过程现象背后的机制具有十分重要的意义。传统光学显微镜受到由光波波长和孔径造成的衍射极限的限制，无法观测细胞器纳米级精细结构及细胞器间相互作用的动态变化规律。超分辨显微成像技术的出现为细胞器相互作用研究提供了重要手段，在深入揭示细胞器相互作用规律，阐明生理病理现象深层的机制研究中发挥了重要的作用。文中介绍了受激发射损耗（Stimulated emission depletion, STED）显微成像、结构光照明显微成像（Structured illumination microscopy, SIM）、单分子定位显微成像（Single molecule localization microscopy, SMLM）技术，并总结了这三类超分辨显微成像技术在细胞器相互作用中的应用与现状，为超分辨显微成像技术在细胞器相互作用研究中的应用提供思路拓展。最后，对超分辨显微成像技术在细胞器相互作用研究中的优势与不足进行分析总结，展望了超分辨显微成像技术在活细胞内细胞器相互作用成像中的需求发展趋势，为光学与医学及生物学的交叉融合发展提供一定的参考。