Source Code for Optical Superresolution Microscopy

Conventional optical microscopes generally provide blurry and indistinguishable images for subwavelength nanostructures. However, a wealth of intensity and phase information is hidden in the corresponding diffraction-limited optical patterns and can be used for the recognition of structural features, such as size, shape, and spatial arrangement. Here, we apply a deep-learning framework to improve the spatial resolution of optical imaging for metal nanostructures with regular shapes yet varied arrangement. A convolutional neural network (CNN) is constructed and pre-trained by the optical images of randomly distributed gold nanoparticles as input and the corresponding scanning-electron microscopy images as ground truth. The CNN is then learned to recover reversely the non-diffracted super-resolution images of both regularly arranged nanoparticle dimers and randomly clustered nanoparticle multimers from their blurry optical images. The profiles and orientations of these structures can also be reconstructed accurately. Moreover, the same network is extended to deblur the optical images of randomly cross-linked silver nanowires. Most sections of these intricate nanowire nets are recovered well with a slight discrepancy near their intersections. This deep-learning augmented microscopy can be applied to significantly enhance the resolving capability of low-magnification scanning-electron microscopy.

传统光学显微镜对于亚波长纳米结构通常只能生成模糊难辨的成像结果。然而，大量的强度与相位信息隐藏在对应的衍射受限光学图样中，可用于识别结构特征，如尺寸、形貌与空间排布。本研究采用深度学习框架，针对形貌规整但排布各异的金属纳米结构，提升其光学成像的空间分辨率。本研究构建了卷积神经网络（Convolutional Neural Network, CNN），以随机分布金纳米颗粒的光学图像作为输入、对应扫描电子显微镜（Scanning Electron Microscopy, SEM）图像作为真值标签进行预训练。随后，该CNN经训练后可从模糊的光学图像中逆向还原出规整排布纳米颗粒二聚体与随机聚集纳米颗粒多聚体的无衍射超分辨率图像，且这些结构的轮廓与取向也可被精准重构。此外，该网络还可拓展用于对随机交联银纳米线的光学图像进行去模糊处理，这些复杂纳米线网络的大部分区段均可被良好还原，仅在交叉点附近存在细微偏差。这种深度学习增强型显微镜技术可用于显著提升低倍扫描电子显微镜的分辨能力。