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Content-aware image restoration for electron microscopy

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Krull,  Alexander
Max Planck Institute for the Physics of Complex Systems, Max Planck Society;

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1810.05420.pdf
(プレプリント), 4MB

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引用

Buchholz, T.-O., Krull, A., Shahidi, R., Pigino, G., Jekely, G., & Jug, F. (2019). Content-aware image restoration for electron microscopy. In T., Müller-Reichert, & G., Pigino (Eds.), Three-Dimensional Electron Microscopy (pp. 277-289). Cambridge, MA: Academic Press. doi:10.1016/bs.mcb.2019.05.001.


引用: https://hdl.handle.net/21.11116/0000-0005-C124-8
要旨
Multiple approaches to use deep neural networks for image restoration have recently been proposed. Training such networks requires well registered pairs of high and low-quality images. While this is easily achievable for many imaging modalities, e.g., fluorescence light microscopy, for others it is not. Here we summarize on a number of recent developments in the fast-paced field of Content-Aware Image Restoration (CARE), in particular, and the associated area of neural network training, more in general. We then give specific examples how electron microscopy data can benefit from these new technologies.