Cnn-Based Deblurring Of Terahertz Images

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.