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  Removing Structured Noise with Self-Supervised Blind-Spot Networks.

Broaddus, C., Krull, A., Weigert, M., Schmidt, U., & Myers, G. (2020). Removing Structured Noise with Self-Supervised Blind-Spot Networks. In IEEE ISBI 2020: International Conference on Biomedical Imaging: April 2-7, 2020, Iowa City, Iowa, USA: symposium proceeding (pp. 159-163). Piscataway, N.J.: IEEE.

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 Creators:
Broaddus, Coleman1, Author           
Krull, Alexander1, Author           
Weigert, Martin1, Author           
Schmidt, Uwe1, Author           
Myers, Gene1, Author           
Affiliations:
1Max Planck Institute for Molecular Cell Biology and Genetics, Max Planck Society, ou_2340692              

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 Abstract: Removal of noise from fluorescence microscopy images is an important first step in many biological analysis pipelines. Current state-of-the-art supervised methods employ convolutional neural networks that are trained with clean (ground-truth) images. Recently, it was shown that self-supervised image denoising with blind spot networks achieves excellent performance even when ground-truth images are not available, as is common in fluorescence microscopy. However, these approaches, e.g. Noise2Void ( N2V), generally assume pixel-wise independent noise, thus limiting their applicability in situations where spatially correlated (structured) noise is present. To overcome this limitation, we present Structured Noise2Void (STRUCTN2V), a generalization of blind spot networks that enables removal of structured noise without requiring an explicit noise model or ground truth data. Specifically, we propose to use an extended blind mask (rather than a single pixel/blind spot), whose shape is adapted to the structure of the noise. We evaluate our approach on two real datasets and show that STRUCTN2V considerably improves the removal of structured noise compared to existing standard and blind-spot based techniques.

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 Dates: 2020-05-22
 Publication Status: Issued
 Pages: -
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 Rev. Type: -
 Identifiers: DOI: 10.1109/ISBI45749.2020.9098336
Other: cbg-7927
 Degree: -

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Title: IEEE 17th International Symposium on Biomedical Imaging (ISBI)
Place of Event: Iowa City, Iowa, USA
Start-/End Date: 2020-04-02 - 2020-04-06

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Title: IEEE ISBI 2020 : International Conference on Biomedical Imaging : April 2-7, 2020, Iowa City, Iowa, USA : symposium proceeding
Source Genre: Proceedings
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Publ. Info: Piscataway, N.J. : IEEE
Pages: - Volume / Issue: IEEE ISBI 2020 : International Conference on Biomedical Imaging : April 2-7, 2020, Iowa City, Iowa, USA : symposium proceeding Sequence Number: - Start / End Page: 159 - 163 Identifier: ISBN: 978-1-5386-9330-8