Machine Learning Techniques for THz Imaging and Time-Domain Spectroscopy

Abstract

Terahertz imaging and time-domain spectroscopy have been widely used to characterize the properties of test samples in various biomedical and engineering fields. Many of these tasks require the analysis of acquired terahertz signals to extract embedded information, which can be achieved using machine learning. Recently, machine learning techniques have developed rapidly, and many new learning models and learning algorithms have been investigated. Therefore, combined with state-of-the-art machine learning techniques, terahertz applications can be performed with high performance that cannot be achieved using modeling techniques that precede the machine learning era. In this review, we introduce the concept of machine learning and basic machine learning techniques and examine the methods for performance evaluation. We then summarize representative examples of terahertz imaging and time-domain spectroscopy that are conducted using machine learning.

Roadmap of Terahertz Imaging 2021

“…Current challenges and possible future trends: One can expect further intensive implementation of DL and other AI instruments into THz imaging and spectroscopic systems [436], both for compact diffractive optics elements design, as well as improvements of recorded data quality. So-called ghost imaging can be one of such application areas [248].…”

Terahertz Imaging for Breast Cancer Detection

“…Recently, numerous chemometrics methods, such as principal component analysis (PCA), partial least square method (PLS), linear discriminant analysis (LDA), decision tree (DT), genetic algorithm (GA), cluster analysis (CA), random forest (RF), partial least squares discrimination analysis (PLS-DA), ANN, and SVM, have been applied in THz imaging to improve THz data collection speed and accuracy. In addition, machine learning techniques have been used to enhance the visualization of THz images [ 81 , 82 , 83 , 84 , 85 , 86 , 87 ].…”

Fast FMCW Terahertz Imaging for In-Process Defect Detection in Press Sleeves for the Paper Industry and Image Evaluation with a Machine Learning Approach

“…Since the surface area of the press sleeves is quite large, manual inspection for possible defects over such an area can become very time consuming and the risk of overlooking smaller defects become quite high. On the other hand, the large sleeve area with a relatively limited number of round defects, which show with good contrast in the acquired terahertz images, constitutes a promising situation for the automated image processing and defect detection by machine learning (ML) approaches [ 39 ]. Application of ML techniques to terahertz measurements has been reported many times before, however, mostly in terms of direct application of the ML methods to the quite complex terahertz signals (in pulsed time-domain systems or continuous-wave systems [ 40 ]) and employing various sophisticated ML concepts such as artificial neural networks (ANNs) [ 39 , 41 ], random forests, support vector machines (SVMs) and many others (see [ 42 ] and references therein).…”

“…On the other hand, the large sleeve area with a relatively limited number of round defects, which show with good contrast in the acquired terahertz images, constitutes a promising situation for the automated image processing and defect detection by machine learning (ML) approaches [ 39 ]. Application of ML techniques to terahertz measurements has been reported many times before, however, mostly in terms of direct application of the ML methods to the quite complex terahertz signals (in pulsed time-domain systems or continuous-wave systems [ 40 ]) and employing various sophisticated ML concepts such as artificial neural networks (ANNs) [ 39 , 41 ], random forests, support vector machines (SVMs) and many others (see [ 42 ] and references therein). There exist only few examples where ML is applied on the acquired terahertz images in an image processing sense, in which a direct evaluation of the image content itself is performed, rather than the measured signals.…”