CNN-Based Deblurring of THz Time-Domain Images

Abstract

In recent years, terahertz (THz) time-domain imaging attracted significant attention and become a useful tool in many applications. A THz time-domain imaging system measures amplitude changes of the THz radiation across a range of frequencies so the absorption coefficient of the materials in the sample can be obtained. THz time-domain images represent 3D hyperspectral cubes with several hundred bands corresponding to different wavelengths i.e., frequencies. Moreover, a THz beam has a non-zero beam waist and therefore introduces band-dependent blurring effects in the resulting images accompanied by system-dependent noise. Removal of blurring effects and noise from the whole 3D hyperspectral cube is addressed in the current work. We will start by introducing THz beam shape effects and its formulation as a deblurring problem, followed by presenting a convolutional neural network (CNN)-based approach which is able to tackle all bands jointly. To the best of our knowledge, this is the first time that a CNN is used to remove the THz beam shape effects from all bands jointly of THz time-domain images. Experiments on synthetic images show that the proposed approach significantly outperforms conventional model-based deblurring methods and band-by-band approaches.