Abstract
Noise reduction is an important issue in measurement. A difficulty to train a noise reduction model using machine learning is that clean signal on measurement object needed for supervised training is hardly available in most advanced measurement problems. Recently, an unsupervised technique for training a noise reduction model called Noise2Noise has been proposed, and a deep learning model named U-net trained by this technique has demonstrated promising performance in some measurement problems. In this study, we applied this technique to highly noisy signals of electric current waveforms obtained by measuring nanoparticle passages in a multistage narrowing nanochannel. We found that a convolutional AutoEncoder (CAE) was more suitable than the U-net for the noise reduction using the Noise2Noise technique in the nanochannel measurement problem.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Tsutsui, M., et al.: High-throughput single nanoparticle detection using a feed-through channel-integrated nanopore. Nanoscale 43, 20475–20484 (2019)
Basty, A.: Shenoy: Introduction to Digital Signal Processing and Filter Design. Wiley, Hoboken (2005)
Ryan, O.: Linear Algebra, Signal Processing, and Wavelets - A Unified Approach. Springer, Heidelberg (2019). https://doi.org/10.1007/978-3-030-02940-1
Grewal, M.S., Andrews, A.P.: Kalman Filtering: Theory and Practice with MATLAB. Wiley - IEEE (2014)
Mao, X.-J., Shen, C., Yang, Y.-B.: Image restoration using convolutional auto-encoders with symmetric skip connections. In: Proceedings of Thirtieth Conference on Neural Information Processing Systems, NIPS 2016 (2016)
Ledig, C., et al.: Photo-realistic single image super-resolution using a generative adversarial net-work. In: Proceedings of Computer Vision and Pattern Recognition, CVPR 2017, pp. 105–114 (2017)
Zhang, R., Isola, P., Efros, A.A.: Colorful image colorization. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9907, pp. 649–666. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46487-9_40
Iizuka, S., Simo-Serra, E., Ishikawa, H.: Globally and locally consistent image completion. ACM Trans. Graph. 36(4), 107:1–107:14 (2017)
Lehtinen, J., et al.: Noise2Noise: learning image restoration without clean data. In: Proceedings of the 35th International Conference on Machine Learning, ICML 2018, PMLR, vol. 80, pp. 2965–2974 (2018)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: Proceedings of the 3rd International Conference on Learning Representations, ICLR 2015 (2015)
Acknowledgement
This research is supported by JST CREST JMPJCR1666.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Takaai, T., Tsutsui, M. (2021). Unsupervised Noise Reduction for Nanochannel Measurement Using Noise2Noise Deep Learning. In: Gupta, M., Ramakrishnan, G. (eds) Trends and Applications in Knowledge Discovery and Data Mining. PAKDD 2021. Lecture Notes in Computer Science(), vol 12705. Springer, Cham. https://doi.org/10.1007/978-3-030-75015-2_5
Download citation
DOI: https://doi.org/10.1007/978-3-030-75015-2_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-75014-5
Online ISBN: 978-3-030-75015-2
eBook Packages: Computer ScienceComputer Science (R0)