CNN-based Deblurring of Terahertz Images

Marina Ljubenović, Shabab Bazrafkan, Jan De Beenhouwer, Jan Sijbers

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 the THz beam shape effects. Experiments on simulated THz images show superior results for the proposed method compared to conventional model-based deblurring methods.

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Paper Citation


in Harvard Style

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 - Volume 4: VISAPP, ISBN 978-989-758-402-2, pages 323-330. DOI: 10.5220/0008973103230330


in Bibtex Style

@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 - Volume 4: VISAPP,},
year={2020},
pages={323-330},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008973103230330},
isbn={978-989-758-402-2},
}


in EndNote Style

TY - CONF

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