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

Abstract

In this paper, 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.

Authors:
 [1];  [1]; ORCiD logo [2]; ORCiD logo [2]
+ Show Author Affiliations
  1. State Univ. of New York (SUNY), Albany, NY (United States)
  2. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Spallation Neutron Source (SNS)
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES). Scientific User Facilities Division
OSTI Identifier:
1633142
Grant/Contract Number:  
AC05-00OR22725
Resource Type:
Accepted Manuscript
Journal Name:
MRS Communications
Additional Journal Information:
Journal Volume: 10; Journal Issue: 1; Journal ID: ISSN 2159-6859
Publisher:
Materials Research Society - Cambridge University Press
Country of Publication:
United States
Language:
English
Subject:
36 MATERIALS SCIENCE

Citation Formats

Chang, Ming-Ching, Wei, Yi, Chen, Wei-Ren, and Do, Changwoo. Deep learning-based super-resolution for small-angle neutron scattering data: attempt to accelerate experimental workflow. United States: N. p., 2020. Web. doi:10.1557/mrc.2019.166.
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Chang, Ming-Ching, Wei, Yi, Chen, Wei-Ren, & Do, Changwoo. Deep learning-based super-resolution for small-angle neutron scattering data: attempt to accelerate experimental workflow. United States. https://doi.org/10.1557/mrc.2019.166
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Chang, Ming-Ching, Wei, Yi, Chen, Wei-Ren, and Do, Changwoo. Tue . "Deep learning-based super-resolution for small-angle neutron scattering data: attempt to accelerate experimental workflow". United States. https://doi.org/10.1557/mrc.2019.166. https://www.osti.gov/servlets/purl/1633142.
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@article{osti_1633142,
title = {Deep learning-based super-resolution for small-angle neutron scattering data: attempt to accelerate experimental workflow},
author = {Chang, Ming-Ching and Wei, Yi and Chen, Wei-Ren and Do, Changwoo},
abstractNote = {In this paper, 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.},
doi = {10.1557/mrc.2019.166},
journal = {MRS Communications},
number = 1,
volume = 10,
place = {United States},
year = {2020},
month = {1}
}
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https://doi.org/10.1557/mrc.2019.166
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Works referenced in this record:

Time-Resolved Small-Angle X-ray Scattering Reveals Millisecond Transitions of a DNA Origami Switch
journal, March 2018

Image Super-Resolution Using Deep Convolutional Networks
journal, February 2016

  • Dong, Chao; Loy, Chen Change; He, Kaiming
  • IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 38, Issue 2
  • DOI: 10.1109/TPAMI.2015.2439281

A Route to the Brightest Possible Neutron Source?
journal, February 2007

Deep learning
journal, May 2015

  • LeCun, Yann; Bengio, Yoshua; Hinton, Geoffrey
  • Nature, Vol. 521, Issue 7553
  • DOI: 10.1038/nature14539

Subdomain Structures of Lamellar and Reverse Hexagonal Pluronic Ternary Systems Investigated by Small Angle Neutron Scattering
journal, April 2009

  • Doe, Changwoo; Jang, Hyung-Sik; Kline, Steven R.
  • Macromolecules, Vol. 42, Issue 7
  • DOI: 10.1021/ma802296u

Kinetics of Polymer–Fullerene Phase Separation during Solvent Annealing Studied by Table-Top X-ray Scattering
journal, February 2017

  • Vegso, Karol; Siffalovic, Peter; Jergel, Matej
  • ACS Applied Materials & Interfaces, Vol. 9, Issue 9
  • DOI: 10.1021/acsami.6b15167

The suite of small-angle neutron scattering instruments at Oak Ridge National Laboratory
journal, February 2018

  • Heller, William T.; Cuneo, Matthew; Debeer-Schmitt, Lisa
  • Journal of Applied Crystallography, Vol. 51, Issue 2
  • DOI: 10.1107/S1600576718001231

Time-Resolved Small-Angle X-ray Scattering Reveals Millisecond Transitions of a DNA Origami Switch
text, January 2018

  • Bruetzel, Linda K.; Walker, Philipp U.; Gerling, Thomas
  • Deutsches Elektronen-Synchrotron, DESY, Hamburg
  • DOI: 10.3204/pubdb-2018-01622

Quantitative analysis of lyotropic lamellar phases SANS patterns in powder oriented samples
journal, October 2005

Molecular Alignment in Polyethylene during Cold Drawing Using In-Situ SANS and Raman Spectroscopy
journal, April 2017

Real-Time Observation of Nonclassical Protein Crystallization Kinetics
journal, January 2015

  • Sauter, Andrea; Roosen-Runge, Felix; Zhang, Fajun
  • Journal of the American Chemical Society, Vol. 137, Issue 4
  • DOI: 10.1021/ja510533x

Unraveling the equilibrium chain exchange kinetics of polymeric micelles using small-angle neutron scattering – architectural and topological effects
journal, March 2007

  • Lund, Reidar; Willner, Lutz; Richter, Dieter
  • Journal of Applied Crystallography, Vol. 40, Issue s1
  • DOI: 10.1107/S0021889807005201

Recent applications of synchrotron radiation and neutrons in the study of soft matter
journal, February 2017

Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network
conference, June 2016

  • Shi, Wenzhe; Caballero, Jose; Huszar, Ferenc
  • 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
  • DOI: 10.1109/CVPR.2016.207

Fully Convolutional Networks for Semantic Segmentation
journal, April 2017

  • Shelhamer, Evan; Long, Jonathan; Darrell, Trevor
  • IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 39, Issue 4
  • DOI: 10.1109/TPAMI.2016.2572683

Reconstruction of three-dimensional anisotropic structure from small-angle scattering experiments
journal, August 2017

The extended Q -range small-angle neutron scattering diffractometer at the SNS
journal, July 2010

Recent experimental and theoretical developments in time-resolved X-ray spectroscopies
journal, October 2014

Event-based processing of neutron scattering data at the Spallation Neutron Source
journal, May 2018

  • Granroth, Garrett E.; An, Ke; Smith, Hillary L.
  • Journal of Applied Crystallography, Vol. 51, Issue 3
  • DOI: 10.1107/S1600576718004727

Fast Image Super-Resolution Based on In-Place Example Regression
conference, June 2013

  • Yang, Jianchao; Lin, Zhe; Cohen, Scott
  • 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
  • DOI: 10.1109/CVPR.2013.141

Trainable Nonlinear Reaction Diffusion: A Flexible Framework for Fast and Effective Image Restoration
journal, June 2017

  • Chen, Yunjin; Pock, Thomas
  • IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 39, Issue 6
  • DOI: 10.1109/TPAMI.2016.2596743

Fully convolutional networks for semantic segmentation
conference, June 2015

  • Long, Jonathan; Shelhamer, Evan; Darrell, Trevor
  • 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
  • DOI: 10.1109/cvpr.2015.7298965

Deep Learning
text, January 2018

Image Super-Resolution Using Deep Convolutional Networks
preprint, January 2015

Fully Convolutional Networks for Semantic Segmentation
preprint, January 2016

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