可微电子显微镜模拟：用于可视化的方法和应用
Differentiable Electron Microscopy Simulation: Methods and Applications for Visualization
AI总结：
本文提出了一种可微的电子显微镜模拟系统，能够以显微图像风格描绘原子模型，用于可视化和逆问题求解，通过参数估计生成更接近真实数据的显微图像。
摘要：
我们提出了一种新的显微镜模拟系统，能够以显微图像风格描绘原子模型，类似于物理电子显微镜成像的结果。该系统可扩展，能够模拟数十个病毒颗粒的电子显微镜，并比以前的方法更快地合成图像。此外，该模拟器是可微的，其确定性和随机性阶段都能生成显微图像中的信号和噪声表示。这一特性使系统能够通过优化解决逆问题，从而利用从真实数据估计的参数生成显微模拟。我们通过两个应用展示了这种学习能力：(1) 估计定义模拟和真实显微图像探测器特性的调制传递函数参数；(2) 基于从模拟示例训练的参数对真实数据进行去噪。尽管当前的模拟器由于其正向设计不支持任何参数估计，但我们展示了使用估计参数的结果与真实显微图像结果非常相似。此外，我们评估了该方法的去噪能力，并显示其结果优于现有最先进方法。去噪后的显微图像在倾斜系列断层扫描重建中噪声更少，最终减少了直接体积渲染显微断层扫描时噪声的视觉主导地位。
英文摘要：
We propose a new microscopy simulation system that can depict atomistic models in a micrograph visual style, similar to results of physical electron microscopy imaging. This system is scalable, able to represent simulation of electron microscopy of tens of viral particles and synthesizes the image faster than previous methods. On top of that, the simulator is differentiable, both its deterministic as well as stochastic stages that form signal and noise representations in the micrograph. This notable property has the capability for solving inverse problems by means of optimization and thus allows for generation of microscopy simulations using the parameter settings estimated from real data. We demonstrate this learning capability through two applications: (1) estimating the parameters of the modulation transfer function defining the detector properties of the simulated and real micrographs, and (2) denoising the real data based on parameters trained from the simulated examples. While current simulators do not support any parameter estimation due to their forward design, we show that the results obtained using estimated parameters are very similar to the results of real micrographs. Additionally, we evaluate the denoising capabilities of our approach and show that the results showed an improvement over state-of-the-art methods. Denoised micrographs exhibit less noise in the tilt-series tomography reconstructions, ultimately reducing the visual dominance of noise in direct volume rendering of microscopy tomograms.
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