Skip to main content
SpringerLink
Log in

Speckle-learning-based object recognition using optical memory effect

  • Regular Paper
  • Published:
Optical Review Aims and scope Submit manuscript

Abstract

We present an efficient construction method for object recognition based on speckle learning using the optical memory effect. An object classifier based on speckle learning without the process of reducing or eliminating scattering and with a simple optical setup has been previously reported, but it requires a large number of training images to improve the performance of the classifier. This method is not applicable for bioimaging because of the difficulty of collecting training images caused by position control and phototoxicity of target cells. In our method, a wide variety of training images are augmented by a computer from a few speckle intensity images in the working range of the optical memory effect. We experimentally demonstrated our method with a 4f-optical system implementing the optical memory effect. As a result, the constructed binary classifier showed high accuracy under various scattering conditions and resolutions of the test image.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Data availability

Data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.

References

  1. Mosk, A.P., Lagendijk, A., Lerosey, G., Fink, M.: Controlling waves in space and time for imaging and focusing in complex media. Nat. Photonics 6, 283–292 (2012)

    Article  ADS  Google Scholar 

  2. Marx, V., Ao, B.O.X.D.I.Y.: Microscopy: hello, adaptive optics. Nat. Methods 14, 1133–1136 (2017)

    Article  Google Scholar 

  3. Yamaguchi, N., Fujii, Y.: Rapid on-site monitoring of bacteria in freshwater environments using a portable microfluidic counting system. Biol. Pharm. Bull. 43, 87–92 (2020)

    Article  Google Scholar 

  4. Tokunaga, Y., Wakabayashi, Y., Yonogi, S., Saito, M., Yamaguchi, N.: Microfluidic rapid quantification of Salmonella enterica serovar Typhimurium collected from chicken meat using immunomagnetic separation after formaldehyde treatment. Int. J. Food Sci. Technol. 56, 5402–5408 (2021)

    Article  Google Scholar 

  5. Narasimhan, S.G.: Structured light methods for underwater imaging: light stripe scanning and photometric stereo. In: Proceedings of OCEANS 2005 MTS/IEEE, vol. 3, pp. 2610–2617. IEEE (2005)

  6. Benxing, G., Wang, G.: Underwater image recovery using structured light. IEEE Access 7, 77183–77189 (2019)

    Article  Google Scholar 

  7. Li, Y., Lu, H., Li, J., Li, X., Li, Y., Serikawa, S.: Underwater image de-scattering and classification by deep neural network. Comput. Electr. Eng. 54, 68–77 (2016)

    Article  Google Scholar 

  8. Popoff, S., Lerosey, G., Fink, M., Boccara, A.C., Gigan, S.: Image transmission through an opaque material. Nat. Commun. 1, 1–5 (2010)

    Article  Google Scholar 

  9. Liutkus, A., Martina, D., Popoff, S., Chardon, G., Katz, O., Lerosey, G., Gigan, S., Daudet, L., Carron, I.: Imaging with nature: compressive imaging using a multiply scattering medium. Sci. Rep. 4, 1–7 (2014)

    Article  Google Scholar 

  10. Antoine Boniface, S.G., Dong, J.: Non-invasive focusing and imaging in scattering media with a fluorescence-based transmission matrix. Nat. Commun. 11, 6154 (2020)

    Article  ADS  Google Scholar 

  11. Bertolotti, J., Van Putten, E.G., Blum, C., Lagendijk, A., Vos, W.L., Mosk, A.P.: Non-invasive imaging through opaque scattering layers. Nature 491, 232–234 (2012)

    Article  ADS  Google Scholar 

  12. Okamoto, Y., Horisaki, R., Tanida, J.: Noninvasive three-dimensional imaging through scattering media by three-dimensional speckle correlation. Opt. Lett. 44, 2526–2529 (2019)

    Article  ADS  Google Scholar 

  13. Horisaki, R., Okamoto, Y., Tanida, J.: Single-shot noninvasive three-dimensional imaging through scattering media. Opt. Lett. 44, 4032–4035 (2019)

    Article  ADS  Google Scholar 

  14. Ehira, K., Horisaki, R., Nishizaki, Y., Naruse, M., Tanida, J.: Spectral speckle-correlation imaging. Appl. Opt. 60, 2388–2392 (2021)

    Article  ADS  Google Scholar 

  15. Ando, T., Horisaki, R., Tanida, J.: Speckle-learning-based object recognition through scattering media. Opt. Express 23, 33902–33910 (2015)

    Article  ADS  Google Scholar 

  16. Takagi, R., Horisaki, R., Tanida, J.: Object recognition through a multi-mode fiber. Opt. Rev. 24, 117–120 (2017)

    Article  Google Scholar 

  17. Sun, Y., Shi, J., Sun, L., Fan, J., Zeng, G.: Image reconstruction through dynamic scattering media based on deep learning. Opt. Express 27, 16032–16046 (2019)

    Article  ADS  Google Scholar 

  18. Laissue, P.P., Alghamdi, R.A., Tomancak, P., Reynaud, E.G., Shroff, H.: Assessing phototoxicity in live fluorescence imaging. Nat. Methods 14, 657–661 (2017)

    Article  Google Scholar 

  19. Weber, M., Waters, J.C., Norden, C.: Phototoxicity in live fluorescence microscopy, and how to avoid it. BioEssays 39, 1–15 (2017)

    Google Scholar 

  20. Alone, B.T., Azzarelli, M.B., Chirato, A.S., Ello, F.D., Icario, V., Iola, D.V., Acchetti, E.J., Regonzio, M.B., Aimondi, R., Erullo, G.C., Olli, D.P.: Phototoxicity induced in living HeLa cells by focused femtosecond laser pulses: a data-driven approach. Biomed. Opt. Express 12, 7886–7905 (2021)

    Article  Google Scholar 

  21. Wang, J., Perez, L.: The effectiveness of data augmentation in image classification using deep learning. arXiv preprint p. arXiv:1712.04621v1 (2017)

  22. Chollet, F.: Xception: deep learning with depthwise separable convolutions. arXiv preprint p. arXiv: 1610.02357v3 (2017)

  23. MIT CBCL face database. http://cbcl.mit.edu/software-datasets/FaceData2.html

  24. Krizhevsky, A., Hinton, G.: Learning multiple layers of features from tiny images. Technical report, University of Toronto (2009)

  25. D.P. Kingma, J. Ba, Adam: a method for stochastic optimization. In: International Conference on Learning Representations (ICLR) (2015)

Download references

Funding

This work was supported by JSPS KAKENHI Grant number JP20H05890.

Author information

Authors and Affiliations

  1. Environmental Technology Research Division, Systems and Control Laboratory, Osaka Research Institute of Industrial Science and Technology, 1-6-50, Morinomiya, Joto-ku, Osaka, 536-8553, Japan

    Yohei Nishizaki, Katsuhisa Kitaguchi & Mamoru Saito

  2. Department of Information and Physical Sciences, Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita, Osaka, 565-0871, Japan

    Jun Tanida

Authors
  1. Yohei Nishizaki
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Katsuhisa Kitaguchi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mamoru Saito
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jun Tanida
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yohei Nishizaki.

Ethics declarations

Conflict of interest

The authors declare that they have no conflicts of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Nishizaki, Y., Kitaguchi, K., Saito, M. et al. Speckle-learning-based object recognition using optical memory effect. Opt Rev 31, 165–169 (2024). https://doi.org/10.1007/s10043-024-00868-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10043-024-00868-6

Keywords

Search

Navigation