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Improving Electron Micrograph Signal-to-Noise with an Atrous Convolutional Encoder-Decoder [pre-print]
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Ede, Jeffrey M. (2018) Improving Electron Micrograph Signal-to-Noise with an Atrous Convolutional Encoder-Decoder [pre-print]. Working Paper. arXiv. (Unpublished)
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Official URL: https://arxiv.org/abs/1807.11234
Abstract
We present an atrous convolutional encoder-decoder trained to denoise 512×512 crops from electron micrographs. It consists of a modified Xception backbone, atrous convoltional spatial pyramid pooling module and a multi-stage decoder. Our neural network was trained end-to-end to remove Poisson noise applied to low-dose (≪ 300 counts ppx) micrographs created from a new dataset of 17267 2048×2048 high-dose (> 2500 counts ppx) micrographs and then fine-tuned for ordinary doses (200-2500 counts ppx). Its performance is benchmarked against bilateral, non-local means, total variation, wavelet, Wiener and other restoration methods with their default parameters. Our network outperforms their best mean squared error and structural similarity index performances by 24.6% and 9.6% for low doses and by 43.7% and 5.5% for ordinary doses. In both cases, our network's mean squared error has the lowest variance. Source code and links to our new high-quality dataset and trained network have been made publicly available at this https URL [https://github.com/Jeffrey-Ede/Electron-Micrograph-Denoiser].
Item Type: | Working or Discussion Paper (Working Paper) | ||||
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Physics | ||||
Journal or Publication Title: | arXIV | ||||
Publisher: | arXiv | ||||
Official Date: | 2018 | ||||
Dates: |
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Number: | 1807.11234 | ||||
Institution: | University of Warwick | ||||
Status: | Not Peer Reviewed | ||||
Publication Status: | Unpublished | ||||
Access rights to Published version: | Open Access (Creative Commons) | ||||
Description: | Preprint on a neural network trained to improve electron micrograph signal-to-noise. It was developed until it outperformed all the traditional algorithms benchmarked against in the paper. It introduces a large new dataset of transmission electron micrographs for machine learnings. |
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