Adaptive compressed sensing for the fast terahertz reflection tomography

Kijun Kim, Dong Gyu Lee, Woo Gyu Ham, Jaseong Ku, Sang Hun Lee, Chang Beom Ahn, Joo Hiuk Son, Hochong Park

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


In this paper, an adaptive compressed sensing is proposed in order to enhance the performance of fast tetrahertz reflection tomography. The proposed method first acquires data at random measurement points in the spatial domain, and estimates the regions in each tomographic image where much degradation is expected. Then, it allocates additional measurement points to those regions, so that more data are acquired adaptively at the regions prone to degradation, thereby improving the quality of the reconstructed tomographic images. The proposed method was applied to the T-ray reflection tomography system, and the image quality enhancement by the proposed method, compared to the conventional method, was verified for the same number of measurement points.

Original languageEnglish
Pages (from-to)806-812
Number of pages7
JournalIEEE Journal of Biomedical and Health Informatics
Issue number4
StatePublished - 2013


  • Compressed sensing (CS)
  • THz tomography
  • Tetrahertz (THz) imaging


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