Abstract
The exit wave is the state of a uniform plane incident electron wave exiting immediately after passing through a specimen and before the atomic-resolution transmission electron microscopy (ARTEM) image is modified by the aberration of the optical system and the incoherence effect of the electron. Although exit-wave reconstruction has been developed to prevent the misinterpretation of ARTEM images, there have been limitations in the use of conventional exit-wave reconstruction in ARTEM studies of the structure and dynamics of two-dimensional materials. In this study, we propose a framework that consists of the convolutional dual-decoder autoencoder to reconstruct the exit wave and denoise ARTEM images. We calculated the contrast transfer function (CTF) for real ARTEM and assigned the output of each decoder to the CTF as the amplitude and phase of the exit wave. We present exit-wave reconstruction experiments with ARTEM images of monolayer graphene and compare the findings with those of a simulated exit wave. Cu single atom substitution in monolayer graphene was, for the first time, directly identified through exit-wave reconstruction experiments. Our exit-wave reconstruction experiments show that the performance of the denoising task is improved when compared to the Wiener filter in terms of the signal-to-noise ratio, peak signal-to-noise ratio, and structural similarity index map metrics.
Highlights
In recent years, researchers have explored many new materials with unique properties
Atomic-resolution transmission electron microscopy (ARTEM) in real-time has become a major tool for observing the structure and dynamics of 2D materials
We present a deep learning framework for exit-wave reconstruction and denoising using the convolutional dual-decoder autoencoder (CDDAE) for atomic-resolution transmission electron microscopy (ARTEM) studies of the structure and dynamics of 2D materials
Summary
Researchers have explored many new materials with unique properties. Increasingly dynamic studies involving the rapid movement of atoms have been performed using ARTEM To overcome this problem, a direct exit-wave reconstruction method using a single defocused image was suggested [7]. This method shows that the exit wave can be reconstructed from a single defocused image by using the correlation of phase and amplitude information within the adjacent space of the object This method is not applicable to ARTEM due to the complex optical system and the incoherence effect of the electron beam between the object and surrounding free space. The output of the total framework is the result of the image simulation calculation, applying CTF to the reconstructed exit wave, which is equivalent to the denoised Input image We demonstrate this CDDAE exit-wave reconstruction and denoising framework through ARTEM images of an experimental graphene dataset acquired by aberration-corrected TEM, which was operated at 80 kV. We evaluate the denoising performance by the signal-to-noise ratio (SNR), peak-signal-to-noise ratio (PSNR), and structural similarity index map (SSIM) compared to the conventional Wiener filter [16]
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