Data‐Driven Computational Super‐Resolution Fluorescence Microscopy for Live‐Cell Imaging

Fluorescence microscopy has emerged as an essential tool for dynamic high‐resolution imaging in life science, owing to its capacity for in vivo observation, precise molecular labeling, and subcellular resolution. This technique encounters two primary challenges in super‐resolution imaging: overcoming the optical diffraction limit in spatial domain to discern intricate biological structures while enhancing imaging speed in temporal domain to capture millisecond‐scale dynamic processes; adapting to complex biological environments for stable observations while minimizing phototoxicity to preserve sample viability. Despite traditional super‐resolution microscopy surpassing the diffraction limit, its reliance on intricate optical systems and high‐power excitation light leads to constrained imaging speed and considerable photodamage, hindering the capacity for prolonged dynamic observation of living samples, thereby rendering deep learning a potential solution. Here, this review systematically assesses the advancements in deep learning enabled super‐resolution fluorescence microscopy, including learning‐strategy (supervised, unsupervised, and zero‐shot) and integration‐strategy based methods. The performance and technical advantages of diverse methods in fluorescence microscopy are thoroughly investigated. This work offers valuable insights for researchers seeking to implement deep learning‐enhanced imaging techniques, potentially accelerating breakthroughs in bioimaging processing and neuroscience research.