Wave function reconstruction from one or two defocus images is promising for live atomic resolution imaging in transmission electron microscopy. However, a robust and accurate reconstruction method we still need more attention. Here, we present a neural-network-based wave function reconstruction method, EWR-NN, that enables accurate wave function reconstruction from only two defocus images. Results from both simulated and two different experimental defocus series show that the EWR-NN method has better performance than the widely-used iterative wave function reconstruction (IWFR) method. Influence of image number, defocus deviation, residual image shifts and noise level were considered to validate the performance of EWR-NN under practical conditions. It is seen that these factors will not influence the arrangement of atom columns in the reconstructed phase images, while they can alter the absolute values of all-atom columns and degrade the contrast of the phase images.

从一幅或两张散焦图像重建波函数对于透射电子显微镜中的实时原子分辨率成像很有希望。然而，稳健且准确的重建方法仍然需要我们更多的关注。在这里，我们提出了一种基于神经网络的波函数重建方法 EWR-NN，该方法可以仅从两个散焦图像进行精确的波函数重建。模拟和两个不同的实验散焦系列的结果表明，EWR-NN 方法比广泛使用的迭代波函数重建 (IWFR) 方法具有更好的性能。考虑图像数量、离焦偏差、残像偏移和噪声水平的影响，以验证EWR-NN在实际条件下的性能。可以看出，这些因素不会影响重建相位图中原子列的排列，但它们会改变全原子列的绝对值并降低相位图像的对比度。