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.
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P. Lindner and T. Zemb(eds): Neutrons, X-Rays and Light: Scattering Methods Applied to Soft Condensed Matter (North-Holland, The Netherlands, 2002).
D. Richter, M. Monkenbusch, A. Arbe, and J. Colmenero: Neutron Spin Echo in Polymer Systems, Vol. 174 (Springer, Berlin, Heidelberg, 2005), p. 1.
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).
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).
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).
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).
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).
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).
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).
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).
Z. Wang, J. Chen, and S.C.H. Hoi: Deep Learning for Image Super-Resolution: A Survey (2019). arXiv:1902.06068 [Cs.CV].
J. Yang: Image super-resolution via sparse representation. IEEE Trans. Image Process. 19, 2861 (2010).
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).
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).
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).
Y. LeCun, Y. Bengio, and G. Hinton: Deep learning. Nature 521, 436 (2015).
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).
A. Krizhevsky, I. Sutskever, and G.E. Hinton: ImageNet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems 1097 (2012).
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).
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).
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).
E. Shelhamer, J. Long, and T. Darrell: Fully convolutional networks for semantic segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39, 640 (2017).
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].
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).
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).
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).
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).
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.
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).
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).
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).
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).
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.
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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