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Dictionary Learning-Based Image Reconstruction for Terahertz Computed Tomography

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Abstract

Terahertz computed tomography (THz CT) demonstrates its advantages in aspects of nonmetallic and nonpolar materials penetration, 3D internal structure visualization, etc. To perform satisfied reconstruction results, it is necessary to obtain complete measurements from many different views. However, this process is time-consuming and we usually obtain incomplete projections for THz CT in practice, which generates artifacts in the final reconstructed images. To address this issue, dictionary learning-based THz CT reconstruction (DLTR) model is proposed in this study. Especially, the image patches are extracted from other state-of-the-art reconstructed images to train the initial dictionary by using the K-SVD algorithm. Then, the dictionary can be adaptively updated during THz CT reconstruction. Finally, the updated dictionary is used for further updating reconstructed images. In order to verify the accuracy and quality of DLTR method, the filtered back-projection (FBP), simultaneous algebraic reconstruction technique (SART), and total variation (TV) reconstruction are chosen as comparisons. The experiment results show that the DLTR method has a good capability for noise suppression and structures preservation.

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Authors and Affiliations

  1. Key Laboratory of Optoelectronic Technology and System, Ministry of Education, Chongqing University, Chongqing, China

    Fasheng Zhong, Weiwen Wu & Fenglin Liu

  2. Engineering Research Center of Industrial Computed Tomography Nondestructive Testing, Ministry of Education, Chongqing University, Chongqing, 400044, China

    Fasheng Zhong, Weiwen Wu & Fenglin Liu

  3. College of Science, Guangdong University of Petrochemical Technology, Maoming, Guangdong, China

    Liting Niu

Authors
  1. Fasheng Zhong
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  2. Liting Niu
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  3. Weiwen Wu
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  4. Fenglin Liu
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Correspondence to Weiwen Wu or Fenglin Liu.

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Zhong, F., Niu, L., Wu, W. et al. Dictionary Learning-Based Image Reconstruction for Terahertz Computed Tomography. J Infrared Milli Terahz Waves 42, 829–842 (2021). https://doi.org/10.1007/s10762-021-00806-6

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