Skip to main content
SpringerLink
Log in

Deep learning-based super-resolution for small-angle neutron scattering data: attempt to accelerate experimental workflow

  • Research Letter
  • Published:
MRS Communications Aims and scope Submit manuscript

Abstract

The authors propose an alternative route to circumvent the limitation of neutron flux using the recent deep learning super-resolution technique. The feasibility of accelerating data collection has been demonstrated by using small-angle neutron scattering (SANS) data collected from the EQ-SANS instrument at Spallation Neutron Source (SNS). Data collection time can be reduced by increasing the size of binning of the detector pixels at the sacrifice of resolution. High-resolution scattering data is then reconstructed by using a deep learning-based super-resolution method. This will allow users to make critical decisions at a much earlier stage of data collection, which can accelerate the overall experimental workflow.

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

Figure 1
Figure 2
Figure 3
Figure 4

Similar content being viewed by others

References

  1. P. Lindner and T. Zemb(eds): Neutrons, X-Rays and Light: Scattering Methods Applied to Soft Condensed Matter (North-Holland, The Netherlands, 2002).

    Google Scholar 

  2. D. Richter, M. Monkenbusch, A. Arbe, and J. Colmenero: Neutron Spin Echo in Polymer Systems, Vol. 174 (Springer, Berlin, Heidelberg, 2005), p. 1.

    Book  Google Scholar 

  3. T. Narayanan, H. Wacklin, O. Konovalov, and R. Lund: Recent applications of synchrotron radiation and neutrons in the study of soft matter. Crystallogr. Rev. 23, 160 (2017).

    Article  CAS  Google Scholar 

  4. C.J. Milne, T.J. Penfold, and M. Chergui: Recent experimental and theoretical developments in time-resolved X-ray spectroscopies. Coord. Chem. Rev. 277–278, 44 (2014).

    Article  Google Scholar 

  5. G.E. Granroth, K. An, H.L. Smith, P. Whitfield, J.C. Neuefeind, J. Lee, W. Zhou, V.N. Sedov, P.F. Peterson, A. Parizzi, H. Skorpenske, S.M. Hartman, A. Huq, and D.L. Abernathy: Event-based processing of neutron scattering data at the Spallation Neutron Source. J. Appl. Crystallogr. 51, 616 (2018).

    Article  CAS  Google Scholar 

  6. R. Lund, L. Willner, D. Richter, H. Iatrou, N. Hadjichristidis, P. Lindner, and IUCr: Unraveling the equilibrium chain exchange kinetics of polymeric micelles using small-angle neutron scattering—architectural and topological effects. J. Appl. Crystallogr. 40, s327 (2007).

    Article  CAS  Google Scholar 

  7. L.K. Bruetzel, P.U. Walker, T. Gerling, H. Dietz, and J. Lipfert: Time-resolved small-angle X-ray scattering reveals millisecond transitions of a DNA origami switch. Nano Lett. 18, 2672 (2018).

    Article  CAS  Google Scholar 

  8. A. Sauter, F. Roosen-Runge, F. Zhang, G. Lotze, R.M.J. Jacobs, and F. Schreiber: Real-time observation of nonclassical protein crystallization kinetics. J. Am. Chem. Soc. 137, 1485 (2015).

    Article  CAS  Google Scholar 

  9. K. Vegso, P. Siffalovic, M. Jergel, P. Nadazdy, V. Nadazdy, and E. Majkova: Kinetics of polymer–fullerene phase separation during solvent annealing studied by table-top X-ray scattering. ACS Appl. Mater. Interfaces 9, 8241 (2017).

    Article  CAS  Google Scholar 

  10. A. Taylor, M. Dunne, S. Bennington, S. Ansell, I. Gardner, P. Norreys, T. Broome, D. Findlay, and R. Nelmes: A route to the brightest possible neutron source? Science 315, 1092 (2007).

    Article  CAS  Google Scholar 

  11. Z. Wang, J. Chen, and S.C.H. Hoi: Deep Learning for Image Super-Resolution: A Survey (2019). arXiv:1902.06068 [Cs.CV].

    Google Scholar 

  12. J. Yang: Image super-resolution via sparse representation. IEEE Trans. Image Process. 19, 2861 (2010).

  13. W. Dong, L. Zhang, G. Shi, and X. Wu: Image deblurring and super-resolution by adaptive sparse domain selection and adaptive regularization. IEEE Trans. Image Process. 20, 1838 (2011).

    Article  Google Scholar 

  14. K.I. Kim and Y. Kwon: Single-image super-resolution using sparse regression and natural image prior. IEEE Trans. Pattern Anal. Mach. Intell. 32, 1127 (2010).

    Article  Google Scholar 

  15. J. Yang, Z. Lin, and S. Cohen: Fast image super-resolution based on in-place example regression. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1059 (2013).

    Google Scholar 

  16. Y. LeCun, Y. Bengio, and G. Hinton: Deep learning. Nature 521, 436 (2015).

    Article  CAS  Google Scholar 

  17. W. Shi, J. Caballero, F. Huszar, J. Totz, A.P. Aitken, R. Bishop, D. Rueckert, and Z. Wang: Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. IEEE Conference on Computer Vision and Pattern Recognition 1874 (2016).

    Google Scholar 

  18. A. Krizhevsky, I. Sutskever, and G.E. Hinton: ImageNet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems 1097 (2012).

    Google Scholar 

  19. Y. Chen and T. Pock: Trainable nonlinear reaction diffusion: a flexible framework for fast and effective image restoration. IEEE Trans. Pattern Anal. Mach. Intell. 39, 1256 (2017).

    Article  Google Scholar 

  20. C. Dong, C.C. Loy, K. He, and X. Tang: Image super-resolution using deep convolutional networks. IEEE Trans. Pattern Anal. Mach. Intell. 38, 295 (2016).

    Article  Google Scholar 

  21. W.T. Heller, M. Cuneo, L. Debeer-Schmitt, C. Do, L. He, L. Heroux, K. Littrell, S.V. Pingali, S. Qian, C. Stanley, V.S. Urban, B. Wu, W. Bras, and IUCr: The suite of small-angle neutron scattering instruments at Oak Ridge National Laboratory. J. Appl. Crystallogr. 51, 242 (2018).

    Article  CAS  Google Scholar 

  22. E. Shelhamer, J. Long, and T. Darrell: Fully convolutional networks for semantic segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39, 640 (2017).

    Article  Google Scholar 

  23. W. Shi, J. Caballero, L. Theis, F. Huszar, A. Aitken, C. Ledig, and Z. Wang: Is the Deconvolution Layer the Same as a Convolutional Layer? (2016). arXiv:1609.07009 [Cs.CV].

    Google Scholar 

  24. A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer: Automatic differentiation in PyTorch. In NIPS-W, Long Beach, USA (2017).

    Google Scholar 

  25. J.K. Zhao, C.Y. Gao, and D. Liu: The extended Q-range small-angle neutron scattering diffractometer at the SNS. J. Appl. Cryst. 43, 1068 (2010).

    Article  CAS  Google Scholar 

  26. F. Castro-Roman, L. Porcar, G. Porte, and C. Ligoure: Quantitative analysis of lyotropic lamellar phases SANS patterns in powder oriented samples. Eur. Phys. J. E 18, 259 (2005).

    Article  CAS  Google Scholar 

  27. C. Doe, H.-S. Jang, S.R. Kline, and S.-M. Choi: Subdomain structures of lamellar and reverse hexagonal pluronic ternary systems investigated by small-angle neutron scattering. Macromolecules 42, 2645 (2009).

    Article  CAS  Google Scholar 

  28. Z. Wang, T. Iwashita, L. Porcar, Y. Wang, Y. Liu, L.E. Sanchez-Diaz, B. Wu, T. Egami, and W.-R. Chen: Dynamically Correlated Region in Sheared Colloidal Glasses Revealed by Neutron Scattering (2017). arXiv:1709.07507.

    Google Scholar 

  29. C.R. López-Barrón, Y. Zeng, J.J. Schaefer, A.P.R. Eberle, T.P. Lodge, and F.S. Bates: Molecular alignment in polyethylene during cold drawing using in-situ SANS and Raman spectroscopy. Macromolecules 50, 3627 (2017).

    Article  Google Scholar 

  30. K. Mortensen: Structural studies of aqueous solutions of PEO—PPO—PEO triblock copolymers, their micellar aggregates and mesophases; a small-angle neutron scattering study. J. Phys. Condens. Matter 8, A103 (1996).

    Article  CAS  Google Scholar 

  31. Z. Wang, C.N. Lam, W.-R. Chen, W. Wang, J. Liu, Y. Liu, L. Porcar, C.B. Stanley, Z. Zhao, K. Hong, and Y. Wang: Fingerprinting molecular relaxation in deformed polymers. Phys. Rev. X 7, 031003 (2017).

    Google Scholar 

  32. G.-R. Huang, Y. Wang, B. Wu, Z. Wang, C. Do, G.S. Smith, W. Bras, L. Porcar, P. Falus, and W.-R. Chen: Reconstruction of three-dimensional anisotropic structure from small-angle scattering experiments. Phys. Rev. E 96, 022612 (2017).

    Article  Google Scholar 

Download references

Acknowledgment

The Research at Oak Ridge National Laboratory’s Spallation Neutron Source was sponsored by the Scientific User Facilities Division, Office of Basic Energy Sciences, U.S. Department of Energy.

Author information

Authors and Affiliations

  1. University at Albany, State University of New York, New York, NY, USA

    Ming-Ching Chang & Yi Wei

  2. Neutron Scattering Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA

    Wei-Ren Chen & Changwoo Do

Authors
  1. Ming-Ching Chang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yi Wei
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wei-Ren Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Changwoo Do
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Changwoo Do.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chang, MC., Wei, Y., Chen, WR. et al. Deep learning-based super-resolution for small-angle neutron scattering data: attempt to accelerate experimental workflow. MRS Communications 10, 11–17 (2020). https://doi.org/10.1557/mrc.2019.166

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1557/mrc.2019.166

Search

Navigation