期刊
NANOMATERIALS
卷 11, 期 12, 页码 -出版社
MDPI
DOI: 10.3390/nano11123305
关键词
super-resolution; scanning electron microscopy (SEM); adversarial learning; AgCl@Ag microstructure
类别
资金
- Key Research and Development Program Projects of Zhejiang Province [2018C03G2011156]
- Research Project of the State Key Laboratory of Industrial Control Technology, Zhejiang University, China [ICT1806]
This study introduces a staged loss-driven neural networks model architecture to transform low-resolution SEM images into super-resolved ones. Experimental results show that compared with other advanced approaches, our model achieves satisfactory results.
Scanning electron microscopy (SEM) plays a crucial role in the characterization of nanoparticles. Unfortunately, due to the limited resolution, existing imaging techniques are insufficient to display all detailed characteristics at the nanoscale. Hardware-oriented techniques are troubled with costs and material properties. Computational approaches often prefer blurry results or produce a less meaningful high-frequency noise. Therefore, we present a staged loss-driven neural networks model architecture to transform low-resolution SEM images into super-resolved ones. Our approach consists of two stages: first, residual channel attention network (RCAN) with mean absolute error (MAE) loss was used to get a better peak signal-to-noise ratio (PSNR). Then, discriminators with adversarial losses were activated to reconstruct high-frequency texture features. The quantitative and qualitative evaluation results indicate that compared with other advanced approaches, our model achieves satisfactory results. The experiment in AgCl@Ag for photocatalytic degradation confirms that our proposed method can bring realistic high-frequency structural detailed information rather than meaningless noise. With this approach, high-resolution SEM images can be acquired immediately without sample damage. Moreover, it provides an enhanced characterization method for further directing the preparation of nanoparticles.
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