CNN-based TEM image denoising from first principles
This addresses noise in TEM images for materials science, but it is incremental as it builds on existing deep learning methods with specific simulations.
The paper tackled TEM image denoising by proposing a CNN-based approach trained on simulated images with four noise types, showing effectiveness in reducing noise across different levels but with limitations in preserving shapes and avoiding artifacts.
Transmission electron microscope (TEM) images are often corrupted by noise, hindering their interpretation. To address this issue, we propose a deep learning-based approach using simulated images. Using density functional theory calculations with a set of pseudo-atomic orbital basis sets, we generate highly accurate ground truth images. We introduce four types of noise into these simulations to create realistic training datasets. Each type of noise is then used to train a separate convolutional neural network (CNN) model. Our results show that these CNNs are effective in reducing noise, even when applied to images with different noise levels than those used during training. However, we observe limitations in some cases, particularly in preserving the integrity of circular shapes and avoiding visible artifacts between image patches. To overcome these challenges, we propose alternative training strategies and future research directions. This study provides a valuable framework for training deep learning models for TEM image denoising.