Scanning Ion Conductance Microscopy (SICM) enables non-destructive imaging of living cells, which makes it highly valuable in life sciences, medicine, pharmacology, and many other fields. However, because of the uncertainty retrace height of SICM hopping mode, the time resolution of SICM is relatively low, which makes the device fail to meet the demands of dynamic scanning. To address above issues, we propose a fast-scanning method for SICM based on an autoencoder network. Firstly, we cut under-sampled images into small image lists. Secondly, we feed them into a self-constructed primitive-autoencoder super-resolution network to compute high-resolution images. Finally, the inferred scanning path is determined using the computed images to reconstruct the real high-resolution scanning path. The results demonstrate that the proposed network can reconstruct higher-resolution images in various super-resolution tasks of low-resolution scanned images. Compared to existing traditional interpolation methods, the average peak signal-to-noise ratio improvement is greater than 7.5823 dB, and the average structural similarity index improvement is greater than 0.2372. At the same time, using the proposed method for high-resolution image scanning leads to a 156.25% speed improvement compared to traditional methods. It opens up possibilities for achieving high-time resolution imaging of dynamic samples in SICM and further promotes the widespread application of SICM in the future.

扫描离子电导显微镜（SICM）能够对活细胞进行无损成像，这使其在生命科学、医学、药理学和许多其他领域具有很高的价值。然而，由于SICM跳频模式回扫高度的不确定性，导致SICM的时间分辨率较低，使得该装置无法满足动态扫描的需求。为了解决上述问题，我们提出了一种基于自动编码器网络的 SICM 快速扫描方法。首先，我们将欠采样图像切割成小图像列表。其次，我们将它们输入到一个自行构建的原始自动编码器超分辨率网络中来计算高分辨率图像。最后，使用计算图像确定推断的扫描路径，以重建真实的高分辨率扫描路径。结果表明，所提出的网络可以在低分辨率扫描图像的各种超分辨率任务中重建更高分辨率的图像。与现有传统插值方法相比，平均峰值信噪比改善大于7.5823 dB，平均结构相似指数改善大于0.2372。同时，使用所提出的方法进行高分辨率图像扫描，与传统方法相比，速度提高了156.25%。它为SICM中实现动态样品的高时间分辨率成像提供了可能性，进一步推动了SICM未来的广泛应用。