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Authors: Marina Ljubenović ; Shabab Bazrafkan ; Jan De Beenhouwer and Jan Sijbers

Affiliation: imec-Vision Lab, Department of Physics, University of Antwerp, Belgium

Keyword(s): THz Imaging, THz-TDS, CNN, Deblurring.

Abstract: The past decade has seen a rapid development of terahertz (THz) technology and imaging. One way of doing THz imaging is measuring the transmittance of a THz beam through the object. Although THz imaging is a useful tool in many applications, there are several effects of a THz beam not fully addressed in the literature such as reflection and refraction losses and the effects of a THz beam shape. A THz beam has a non-zero waist and therefore introduces blurring in transmittance projection images which is addressed in the current work. We start by introducing THz time-domain images that represent 3D hyperspectral cubes and artefacts present in these images. Furthermore, we formulate the beam shape effects removal as a deblurring problem and propose a novel approach to tackle it by first denoising the hyperspectral cube, followed by a band by band deblurring step using convolutional neural networks (CNN). To the best of our knowledge, this is the first time that a CNN is used to reduce t he THz beam shape effects. Experiments on simulated THz images show superior results for the proposed method compared to conventional model-based deblurring methods. (More)

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Paper citation in several formats:
Ljubenović, M.; Bazrafkan, S.; De Beenhouwer, J. and Sijbers, J. (2020). CNN-based Deblurring of Terahertz Images. In Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 4: VISAPP; ISBN 978-989-758-402-2; ISSN 2184-4321, SciTePress, pages 323-330. DOI: 10.5220/0008973103230330

@conference{visapp20,
author={Marina Ljubenović. and Shabab Bazrafkan. and Jan {De Beenhouwer}. and Jan Sijbers.},
title={CNN-based Deblurring of Terahertz Images},
booktitle={Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 4: VISAPP},
year={2020},
pages={323-330},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008973103230330},
isbn={978-989-758-402-2},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 4: VISAPP
TI - CNN-based Deblurring of Terahertz Images
SN - 978-989-758-402-2
IS - 2184-4321
AU - Ljubenović, M.
AU - Bazrafkan, S.
AU - De Beenhouwer, J.
AU - Sijbers, J.
PY - 2020
SP - 323
EP - 330
DO - 10.5220/0008973103230330
PB - SciTePress

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