{Reference Type}: Journal Article
{Title}: Exit wave function reconstruction from two defocus images using neural network.
{Author}: Meng Z;Ming W;He Y;Shen R;Chen J;
{Journal}: Micron
{Volume}: 177
{Issue}: 0
{Year}: 2024 Feb
{Factor}: 2.381
{DOI}: 10.1016/j.micron.2023.103564
{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.