CNN-based TEM image denoising from first principles

TL;DR

This paper presents a CNN-based method for denoising TEM images using simulated data generated from first principles, demonstrating effectiveness across various noise levels and discussing limitations and future improvements.

Contribution

Introduces a novel deep learning framework trained on simulated TEM images with different noise types, advancing noise reduction techniques in electron microscopy.

Findings

CNNs effectively reduce noise in TEM images

Models generalize to different noise levels

Limitations include shape distortion and artifacts

Abstract

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.