用理论模拟生成真实噪声数据，训练CNN提升电镜图像去噪效果。
CNN-based TEM image denoising from first principles
- 基于密度泛函理论生成高精度图像，模拟四类噪声构建训练集
- 训练的CNN可有效降噪，对不同噪声水平有较好泛化能力
- 关注圆形结构保真与拼接伪影，提出改进方向供后续研究
透射电子显微镜（TEM）图像常受噪声干扰，影响解读。为此，我们提出一种基于深度学习的方法，利用模拟图像进行训练。通过采用一组拟原子轨道基组的密度泛函理论计算，生成高精度真实图像，并引入四种类型噪声构建真实感训练数据集。每种噪声对应训练一个独立的卷积神经网络（CNN）模型。结果表明，这些CNN在降低噪声方面表现有效，即使应用于训练时未覆盖的噪声水平也具备良好泛化能力。然而，部分情况下存在圆形单元保真度下降及图像块间可见伪影的问题。针对上述挑战，我们提出替代训练策略与未来研究方向。本研究为训练用于TEM图像去噪的深度学习模型提供了一个有价值的框架。
原文摘要 · Abstract (English)
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.
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