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Abstract：
Biological super-resolution microscopy is a new generation of imaging techniques that overcome the ~200 nm diffraction limit of conventional light microscopy in spatial resolution. By providing novel spatial or spatiotemporal information on biological processes at nanometer resolution with molecular specificity, it plays an increasingly important role in biomedical sciences. However, its technical constraints also require trade-offs to balance its spatial resolution, temporal resolution, and light exposure of samples. Recently, deep learning has achieved breakthrough performance in many image processing and computer vision tasks. It has also shown great promise in pushing the performance envelope of biological super-resolution microscopy. In this brief review, we survey recent advances in using deep learning to enhance the performance of biological super-resolution microscopy, focusing primarily on computational reconstruction of super-resolution images. Related key technical challenges are discussed. Despite the challenges, deep learning is expected to play an important role in the development of biological super-resolution microscopy. We conclude with an outlook into the future of this new research area.
摘要：
生物超分辨率显微镜是新一代的成像技术，克服了传统光学显微镜在空间分辨率上的~200nm衍射极限。通过以分子特异性的纳米分辨率提供有关生物过程的新空间或时空信息，它在生物医学中起着越来越重要的作用。然而,它的技术限制也需要权衡以平衡其空间分辨率，时间分辨率,和样品的曝光。最近,深度学习在许多图像处理和计算机视觉任务中取得了突破性的性能。它在推动生物超分辨率显微镜的性能方面也显示出巨大的希望。在这个简短的审查，我们调查了使用深度学习来提高生物超分辨率显微镜性能的最新进展，主要关注超分辨率图像的计算重建。讨论了相关的关键技术挑战。尽管面临挑战,深度学习有望在生物超分辨率显微镜的发展中发挥重要作用。最后，我们对这一新研究领域的未来进行了展望。
