Applied Microscopy

  • 제50권
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  • Pages.23.1-23.9
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  • 2020
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  • 2287-5123(pISSN)
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  • 2287-4445(eISSN)
한국현미경학회 (Korean Society of Microscopy)
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DOI QR코드

DOI QR Code

Noise2Atom: unsupervised denoising for scanning transmission electron microscopy images

  • Feng Wang (Electron Microscopy Center, Empa, Swiss Federal Laboratories for Materials Science and Technology) ;
  • Trond R. Henninen (Electron Microscopy Center, Empa, Swiss Federal Laboratories for Materials Science and Technology) ;
  • Debora Keller (Electron Microscopy Center, Empa, Swiss Federal Laboratories for Materials Science and Technology) ;
  • Rolf Erni (Electron Microscopy Center, Empa, Swiss Federal Laboratories for Materials Science and Technology)
  • 투고 : 2020.08.06
  • 심사 : 2020.09.17
  • 발행 : 2020.12.31
⟨ 이전 논문 다음 논문 ⟩

초록

We propose an effective deep learning model to denoise scanning transmission electron microscopy (STEM) image series, named Noise2Atom, to map images from a source domain 𝓢 to a target domain 𝓒, where 𝓢 is for our noisy experimental dataset, and 𝓒 is for the desired clear atomic images. Noise2Atom uses two external networks to apply additional constraints from the domain knowledge. This model requires no signal prior, no noise model estimation, and no paired training images. The only assumption is that the inputs are acquired with identical experimental configurations. To evaluate the restoration performance of our model, as it is impossible to obtain ground truth for our experimental dataset, we propose consecutive structural similarity (CSS) for image quality assessment, based on the fact that the structures remain much the same as the previous frame(s) within small scan intervals. We demonstrate the superiority of our model by providing evaluation in terms of CSS and visual quality on different experimental datasets.

키워드

과제정보

We acknowledge founding from the European Research Council (ERC) under the European Union's Horizon 2020 program (grant agreement No. 681312).

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