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Enhancement of neutron radiography resolution through residual hybrid attention network.

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Title: Enhancement of neutron radiography resolution through residual hybrid attention network.
Authors: Ma, Yuhua1,2 (AUTHOR), Li, Hang1 (AUTHOR), Yin, Wei1 (AUTHOR), Yang, Xin1 (AUTHOR), Huang, Hongwen1 (AUTHOR) hhw@caep.cn, Chen, Hongli2 (AUTHOR) hlchen1@ustc.edu.cn
Source: Nondestructive Testing & Evaluation. May2026, Vol. 41 Issue 5, p2537-2555. 19p.
Subjects: Neutron radiography, Spatial resolution, Nondestructive testing, Image enhancement (Imaging systems), Artificial neural networks, Deep learning, High resolution imaging
Abstract: Neutron radiography is widely used in aviation, nuclear energy, metallurgy, geology and other fields. Improving the image quality and spatial resolution of neutron radiography is crucial to promoting the development of non-destructive testing. This paper proposes a novel solution based on deep learning to improve the quality of neutron radiography. In order to train a more reliable super-resolution network, a real neutron image dataset was constructed using the reactor neutron radiography facility. This dataset contains neutron images accumulated experimentally over the past decade, which amalgamates numerous imaging conditions and various sample types. Based on deep learning, this paper proposes a hybrid attention network that can efficiently extract multi-scale information. The multi-scale branch, residual-in-residual, channel attention and spatial attention can effectively enhance the expressive ability of the network. After two stages of transfer learning, the network demonstrated excellent performance. Both subjective vision and objective scores showed that the network has outstanding abilities to diminish blur artefacts, restore texture details, and enhance resolution. The solution based on deep learning reduces the point diffusion effect in the neutron space detection mechanism, enhances the resolution of neutron radiography, and further improves the accuracy of non-destructive testing. HIGHLIGHT: Establishing the first real neutron image dataset based on reactor neutron radiography facility. Based on deep learning, breaking through the point diffusion effect in neutron scintillation screen, significantly improving clarity. Efficiently improve resolution and recovery details in neutron images using hybrid attention mechanism. Achieve superior spatial resolution and diminish blur artefacts for neutron radiography. [ABSTRACT FROM AUTHOR]
Copyright of Nondestructive Testing & Evaluation is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Enhancement of neutron radiography resolution through residual hybrid attention network.
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  Data: <searchLink fieldCode="AR" term="%22Ma%2C+Yuhua%22">Ma, Yuhua</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Hang%22">Li, Hang</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yin%2C+Wei%22">Yin, Wei</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yang%2C+Xin%22">Yang, Xin</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Huang%2C+Hongwen%22">Huang, Hongwen</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> hhw@caep.cn</i><br /><searchLink fieldCode="AR" term="%22Chen%2C+Hongli%22">Chen, Hongli</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> hlchen1@ustc.edu.cn</i>
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  Data: <searchLink fieldCode="JN" term="%22Nondestructive+Testing+%26+Evaluation%22">Nondestructive Testing & Evaluation</searchLink>. May2026, Vol. 41 Issue 5, p2537-2555. 19p.
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  Data: <searchLink fieldCode="DE" term="%22Neutron+radiography%22">Neutron radiography</searchLink><br /><searchLink fieldCode="DE" term="%22Spatial+resolution%22">Spatial resolution</searchLink><br /><searchLink fieldCode="DE" term="%22Nondestructive+testing%22">Nondestructive testing</searchLink><br /><searchLink fieldCode="DE" term="%22Image+enhancement+%28Imaging+systems%29%22">Image enhancement (Imaging systems)</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22High+resolution+imaging%22">High resolution imaging</searchLink>
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  Data: Neutron radiography is widely used in aviation, nuclear energy, metallurgy, geology and other fields. Improving the image quality and spatial resolution of neutron radiography is crucial to promoting the development of non-destructive testing. This paper proposes a novel solution based on deep learning to improve the quality of neutron radiography. In order to train a more reliable super-resolution network, a real neutron image dataset was constructed using the reactor neutron radiography facility. This dataset contains neutron images accumulated experimentally over the past decade, which amalgamates numerous imaging conditions and various sample types. Based on deep learning, this paper proposes a hybrid attention network that can efficiently extract multi-scale information. The multi-scale branch, residual-in-residual, channel attention and spatial attention can effectively enhance the expressive ability of the network. After two stages of transfer learning, the network demonstrated excellent performance. Both subjective vision and objective scores showed that the network has outstanding abilities to diminish blur artefacts, restore texture details, and enhance resolution. The solution based on deep learning reduces the point diffusion effect in the neutron space detection mechanism, enhances the resolution of neutron radiography, and further improves the accuracy of non-destructive testing. HIGHLIGHT: Establishing the first real neutron image dataset based on reactor neutron radiography facility. Based on deep learning, breaking through the point diffusion effect in neutron scintillation screen, significantly improving clarity. Efficiently improve resolution and recovery details in neutron images using hybrid attention mechanism. Achieve superior spatial resolution and diminish blur artefacts for neutron radiography. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Nondestructive Testing & Evaluation is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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        Value: 10.1080/10589759.2025.2509724
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      – Code: eng
        Text: English
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        PageCount: 19
        StartPage: 2537
    Subjects:
      – SubjectFull: Neutron radiography
        Type: general
      – SubjectFull: Spatial resolution
        Type: general
      – SubjectFull: Nondestructive testing
        Type: general
      – SubjectFull: Image enhancement (Imaging systems)
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      – SubjectFull: Artificial neural networks
        Type: general
      – SubjectFull: Deep learning
        Type: general
      – SubjectFull: High resolution imaging
        Type: general
    Titles:
      – TitleFull: Enhancement of neutron radiography resolution through residual hybrid attention network.
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            NameFull: Ma, Yuhua
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            – D: 01
              M: 05
              Text: May2026
              Type: published
              Y: 2026
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