In Life Science research, electron microscopy (EM) is an essential tool for morphological analysis at the subcellular level as it allows for visualization at nanometer resolution. However, electron micrographs contain image degradations such as noise and blur caused by electromagnetic interference, electron counting errors, magnetic lens imperfections, electron diffraction, etc. These imperfections in raw image quality are inevitable and hamper subsequent image analysis and visualization. In an effort to mitigate these artefacts, many electron microscopy image restoration algorithms have been proposed in the last years. Most of these methods rely on generic assumptions on the image or degradations and are therefore outperformed by advanced methods that are based on more accurate models. Ideally, a method will accurately model the specific degradations that fit the physical acquisition settings. In this overview paper, we discuss different electron microscopy image degradation solutions and demonstrate that dedicated artefact regularisation results in higher quality restoration and is applicable through recently developed probabilistic methods.

中文翻译：

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电子显微镜中最先进的图像恢复概述

在生命科学研究中，电子显微镜 (EM) 是亚细胞水平形态学分析的重要工具，因为它允许以纳米分辨率进行可视化。然而，电子显微照片包含由电磁干扰、电子计数误差、磁透镜缺陷、电子衍射等引起的噪声和模糊等图像质量下降。这些原始图像质量的缺陷是不可避免的，并且会阻碍后续的图像分析和可视化。为了减轻这些伪影，在过去几年中提出了许多电子显微镜图像恢复算法。大多数这些方法依赖于对图像或退化的一般假设，因此优于基于更准确模型的高级方法。理想情况下，一种方法将准确地模拟适合物理采集设置的特定退化。在这篇概述论文中，我们讨论了不同的电子显微镜图像退化解决方案，并证明了专用的伪影正则化可以实现更高质量的恢复，并且适用于最近开发的概率方法。