显微镜的光学孔径和测量带宽的有限性限制了生物应用中的信息获取，包括在观测生物体系的精细亚细胞结构动力学过程、活体超快瞬态生物学过程，以及介观离体组织的高效三维成像等，这一问题成为多领域生物医学研究的制约因素。传统荧光显微镜的局限性促使研究人员着手探索新型荧光显微成像原理和方法。研究者们引入了人工智能手段，以提高荧光显微成像的速度和精度，从而增加信息获取的通量。本文以细胞生物学、发育生物学和肿瘤医学为视角，详细分析了在这些领域中通量限制带来的挑战。结合深度学习，突破了传统荧光显微成像的通量限制问题，为物理光学和图像处理领域的进一步发展提供了契机。这一创新助力于生物医学研究的推进，使科学家能够更全面、深入地理解生命和健康领域的复杂现象。因此，本研究不仅对生物医学领域具有重要意义，而且为未来的研究和应用提供了崭新的可能性。

The restricted optical aperture and limited measurement bandwidth of microscopy impose constraints on information acquisition, particularly during the observation of dynamic processes within fine subcellular structures and ultrafast and transient biological events in vivo, and efficient three-dimensional imaging of mesoscopic ex vivo tissues within biological systems. This limitation represents a formidable hurdle in the landscape of multidisciplinary biomedical research. Traditional constraints associated with fluorescence microscopy have prompted studies on innovative principles and methodologies. By integrating artificial intelligence, efforts have been directed toward enhancing the speed and precision of fluorescence microscopy imaging, thereby augmenting information throughput. In this study, a meticulous analysis of problems posed by throughput limitations encountered in the fields of cell biology, developmental biology, and tumor medicine. Through the integration of artificial intelligence, traditional constraints associated with fluorescence microscopy throughput were surmounted. This pioneering approach paves the way for the advancement of physical optics and image processing and greatly contributes to the evolution of biomedical research. This study offers comprehensive insights into intricate phenomena within the realms of life and health, not only holding paramount importance for biomedical exploration but also unveiling promising avenues for future studies and applications.