This study shows that convolutional neural networks (CNNs) can be used to improve the performance of structured illumination microscopy to enable it to reconstruct a super-resolution image using three instead of nine raw frames, which is the standard number of frames required to this end. Owing to the isotropy of the fluorescence group, the correlation between the high-frequency information in each direction of the spectrum is obtained by training the CNNs. A high-precision super-resolution image can thus be reconstructed using accurate data from three image frames in one direction. This allows for gentler super-resolution imaging at higher speeds and weakens phototoxicity in the imaging process.

中文翻译：

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通过深度学习进行快速结构照明显微镜

这项研究表明，可以使用卷积神经网络（CNN）来提高结构照明显微镜的性能，使其能够使用三个而不是九个原始帧来重建超分辨率图像，这是为此目的所需的标准帧数。由于荧光基团的各向同性，通过训练CNN可以得到光谱各个方向的高频信息之间的相关性。因此，可以使用来自一个方向上三个图像帧的准确数据来重建高精度超分辨率图像。这样可以在较高的速度下进行柔和的超分辨率成像，并在成像过程中减弱光毒性。