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A terahertz time-domain super-resolution imaging method using a local-pixel graph neural network for biological products.
Lei, Tong; Tobin, Brian; Liu, Zihan; Yang, Shu-Yi; Sun, Da-Wen.
Afiliação
  • Lei T; Food Refrigeration and Computerized Food Technology (FRCFT), Agriculture and Food Science Centre, University College Dublin, National University of Ireland, Belfield, Dublin 4, Ireland.
  • Tobin B; UCD Forestry, School of Agriculture and Food Science, University College Dublin, Belfield, Dublin 4, Ireland.
  • Liu Z; Plant Breeding, Wageningesn University and Research, Droevendaalsesteeg 1, Wageningen, the Netherlands.
  • Yang SY; UCD Forestry, School of Agriculture and Food Science, University College Dublin, Belfield, Dublin 4, Ireland.
  • Sun DW; Food Refrigeration and Computerized Food Technology (FRCFT), Agriculture and Food Science Centre, University College Dublin, National University of Ireland, Belfield, Dublin 4, Ireland. Electronic address: dawen.sun@ucd.ie.
Anal Chim Acta ; 1181: 338898, 2021 Oct 09.
Article em En | MEDLINE | ID: mdl-34556238
ABSTRACT
The low image acquisition speed of terahertz (THz) time-domain imaging systems limits their application in biological products analysis. In the current study, a local pixel graph neural network was built for THz time-domain imaging super-resolution. The method could be applied to the analysis of any heterogeneous biological products as it only required a small number of sample images for training and particularly it focused on THz feature frequencies. The graph network applied the Fourier transform to graphs extracted from low-resolution (LR) images bringing an invariance of rotation and flip for local pixels, and the network then learnt the relationship between the state of graphs and the corresponding pixels to be reconstructed. With wood cores and seeds as examples, the images of these samples were captured by a THz time-domain imaging system for training and analysed by the method, achieving the root mean square error (RMSE) of pixels of 0.0957 and 0.1061 for the wood core and seed images, respectively. In addition, the reconstructed high-resolution (HR) images, LR images and true HR images at several feature frequencies were also compared in the current study. Results indicated that the method could not only reconstruct the spatial details and the useful signals from high noise signals at high feature frequencies but could also operate super-resolution in both spatial and spectral aspects.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Produtos Biológicos Idioma: En Ano de publicação: 2021 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Produtos Biológicos Idioma: En Ano de publicação: 2021 Tipo de documento: Article