# Frequency Range Optimization for Continuous Wave Terahertz Imaging
###### Abstract  
With shorter wavelengths than microwaves and greater penetration depth than infrared light, waves in the terahertz spectrum offer unique material testing opportunities. Terahertz technology offers non-invasive and non-destructive testing in the form of spectroscopy and imaging. The most used systems for terahertz imaging are time-domain spectroscopy systems. However, frequency domain spectroscopy systems could offer excellent frequency resolution and be more suitable for biomedical applications. Terahertz imaging based on frequency domain spectroscopy systems is slow, and suffers from frequency tuning errors. A novel one-dimensional imaging principle is presented in this paper. In addition, frequency range optimization based on convolutional neural networks and occlusion sensitivity is utilized for frequency range optimization. Frequency range optimization is used to determine the optimal frequency range for data acquisition. The optimal frequency range or bandwidth should be wide enough for effective phase detection, and should be at the intersection of several spectral footprints in the observed medium. The intersection of spectral footprints is estimated using the proposed frequency range optimization algorithm based on a convolutional neural network and occlusion sensitivity algorithm. The proposed algorithm selects the most sensitive frequency band of THz spectrum automatically, and enables very fast acquisitions for object inspection and classification. terahertz; spectroscopy; imaging; convolutional neural network; occlusion sensitivity; optimization +
Footnote †: journal: Applied Sciences
in THz-TDS systems, but the acquisition speed is much lower. Some of the main advantages of THz-FDS systems are their cost, flexibility, and lower impact from the surroundings, since THz-FDS systems can be implemented using fiber optics. In recent years, THz imaging techniques have been used for material inspection and testing. The most works were done using TDS THz systems, and different techniques were used for image acquisition, such as raster imaging [6], Synthetic Aperture Radar (SAR) [7], compressive imaging [8], tomography [9], and others. The most used imaging techniques in THz imaging are based on well-known principles, while implemented in the reflection mode. Shorter wavelengths compared to microwaves and longer penetration depth compared to the infrared spectrum offers unique possibilities for applications using THz waves. Imaging applications were deployed in the fields of medicine [6], security [10], and others. Researchers proposed different approaches to image processing and enhancement from other imaging fields, such as compressive imaging [11] and deconvolution [12]. THz-TDS systems are the main opto-electronic methods in current THz imaging applications. However, despite the THz-FDS shortcomings, some applications were developed using THz-FDS systems, as shown in [13]. There could be some advantages in using THz-FDS systems, as shown in [14]. Authors in [13] divided imaging based on THz-FDS systems into single-point scanning, full-field imaging, and three-dimensional imaging. Single-point scanning or raster imaging has a focusing lens and scans the observed object pixel-by-pixel. The system for single point scanning utilizes optics for focusing the THz beam to an as small as possible pixel, and can be implemented in reflection mode or transmission mode. Full-field imaging utilizes an array of receivers. A wider THz beam is generated, and then sampled using the full-field receiver. The resolution can be enhanced using compressive imaging or other techniques. Three-dimensional imaging uses more advanced and expensive devices. Examples of three-dimensional imaging are holographic imaging [15] and tomography [16]. A new approach to THz imaging was proposed in the preliminary work [17]. The proposed method used a deconvolution technique with a combination of single-point imaging and full-field imaging. A wide and collimated THz beam was generated and detected with the single-point receiver.