Abstract：
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.
摘要：
从一个或两个散焦图像重建波函数有望用于透射电子显微镜中的实时原子分辨率成像。然而,一种稳健而准确的重建方法还需要我们更多的关注。这里,提出了一种基于神经网络的波函数重构方法,EWR-NN,这使得仅从两个散焦图像进行准确的波函数重建。模拟和两个不同的实验散焦序列的结果表明，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。图像数量的影响，散焦偏差，考虑了残余图像偏移和噪声水平，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像中原子列的排列，同时它们可以改变所有原子列的绝对值并降低相位图像的对比度。
