论文标题
使用深度学习改善SIM中的轴向分辨率
Improving axial resolution in SIM using deep learning
论文作者
论文摘要
结构化照明显微镜是图像实时和固定生物结构的广泛方法，小于常规光学显微镜的衍射极限。利用图像通过深度学习模型的最新进展提高，我们演示了一种方法，可以通过传统的SIM重建来重建3D SIM卡图像堆栈，轴向分辨率的两倍。我们进一步评估了我们对噪声和普遍性的鲁棒性和对变化的样本的耐用性的方法，并讨论了该方法的潜在适应性，以进一步改善分辨率。
Structured Illumination Microscopy is a widespread methodology to image live and fixed biological structures smaller than the diffraction limits of conventional optical microscopy. Using recent advances in image up-scaling through deep learning models, we demonstrate a method to reconstruct 3D SIM image stacks with twice the axial resolution attainable through conventional SIM reconstructions. We further evaluate our method for robustness to noise & generalisability to varying observed specimens, and discuss potential adaptions of the method to further improvements in resolution.
