An overview of state‐of‐the‐art image restoration in electron microscopy

• Published in: Journal of microscopy (Oxford) 2018-09, Vol.271 (3), p.239-254
• Main Authors: ROELS, J., AELTERMAN, J., LUONG, H.Q., LIPPENS, S., PIŽURICA, A., SAEYS, Y., PHILIPS, W.
• Format: Article
• Language: English
• Subjects: Algorithms; Artefacts; Deconvolution; Defects; denoising; Electromagnetic interference; Electron diffraction; Electron micrographs; Electron microscopy; Image acquisition; Image analysis; Image degradation; Image processing; Image quality; Image resolution; Image restoration; Life sciences; Mathematical models; Micrography; Microscopy; Probabilistic methods; Regularization; Visualization
• ISSN: 0022-2720; 1365-2818
• DOI: 10.1111/jmi.12716

Summary 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. Lay description In Life Science research electron microscopy (EM) is an essential tool for analysis at the subcellular level as it allows for visualization at high resolution. However, EM images contain errors such as noise and blur caused by both physical and practical limitations of the device. These imperfections in raw image quality are inevitable and hamper subsequent image analysis and visualization. In an effort to mitigate these artefacts, many EM 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 EM image degradation solutions and demonstrate that dedicated artefact regularization results in higher quality restoration and is applicable through recently developed probabilistic methods.