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Title
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CNN-based deblurring of terahertz images
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Author
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Abstract
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| 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 th e 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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Language
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English
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Source (book)
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Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, 2020 , Valletta, Malta
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Publication
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Setubal
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Scitepress
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2020
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ISBN
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978-989-758-402-2
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DOI
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10.5220/0008973103230330
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Volume/pages
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(2020)
, p. 323-330
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ISI
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000576663400037
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Full text (Publisher's DOI)
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Full text (open access)
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