显微镜
光学
图像分辨率
卷积神经网络
分辨率(逻辑)
材料科学
纳米结构
光学显微镜
纳米颗粒
衍射
摄影术
纳米尺度
扫描电子显微镜
人工智能
计算机科学
纳米技术
物理
作者
Xin Hu,Xuhui Jia,Kai Zhang,Tsz Wing Lo,Yulong Fan,Danjun Liu,Jing Wen,Hongwei Yong,Mohsen Rahmani,Lei Zhang,Dangyuan Lei
出处
期刊:Optics Express
[The Optical Society]
日期:2023-12-22
卷期号:32 (1): 879-879
摘要
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 framework opens new opportunities for computational super-resolution optical microscopy with many potential applications in the fields of bioimaging and nanoscale fabrication and characterization. It could also be applied to significantly enhance the resolving capability of low-magnification scanning-electron microscopy.
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