# Fast Terahertz Coded-Aperture Imaging Based on Convolutional Neural Network  
[PERSON]  
1 College of Electronic Science and Technology, National University of Defense Technology,  
Changsha 410073, China  
1  
[PERSON]  
as high resolution, all-time functionality, low complexity, and low cost and so forth. Besides, the forward-looking and staring imaging capability can be obtained without relying on any relative motion between imaging platform and target, which is different from synthetic aperture radar [16]. As a result, the TCAI technique has a potential application in security screening, battlefield reconnaissance, nondestructive detection and so on.  
In recent years, many methods and systems have been proposed that promote the development of TCAI. The authors of Reference [17] designed a single-pixel pulsed terahertz camera, which utilizes random patterns for imaging and the theory of compression sensing (CS) [18] to solve the imaging equation. In 2013, metamaterial apertures that support custom-designed complex measurement modes were introduced into microwave imaging, which avoids mechanical scanning [15]. [PERSON] et al. utilized randomly programmable metasurface to solve the inverse-scattering problem in the single-sensor imaging [19]. Furthermore, the matrix-imaging equation based on the theory of physical optics was derived and high-resolution TCAI was achieved [20]. Besides, the Bayesian estimation [21] and sparsity-driven methods [22] have greatly inspired TCAI research. Although these methods and systems are effective, there are still some great challenges in TCAI. First, the accuracy of system modeling cannot easily be guaranteed whether it is based on the time-delay signal model [23] or Fresnel diffraction theory [20], and some errors are introduced into the reference signal matrix, which leads to the ability to solve the target scattering coefficient is poor at low SNR. Second, the iterative algorithms are used to reconstruct the target, which are too time-consuming to high frame rate imaging, and they are not quite stable and robust to modeling errors and noise. Considering the importance of these problems in practical applications, a fast TCAI method needs to be devised.  
Deep learning (DL) has greatly inspired the research in object detection, image classification, signal processing, and among many others. For the inverse problem community, learning-based methods have been successfully employed in multiple scattering media imaging [24], holographic image reconstruction [25], lensless computational imaging [26], computational ghost imaging [27, 28] and so forth, but they usually take a lot of effort to collect data set, which is not easily affordable. To reduce the cost of training, some researchers have proposed training imaging network with simulation data set. In References [29] and [30], the practically usable networks that were trained using simulation data set show competitive imaging performance in real-world scenarios, and the simulation results of Reference [31] also demonstrate the effectiveness using simulation data set to train the network. Thus, we investigated the TCAI based on DL to tackle a series of problems mentioned above. In this paper, we design an end to end neural network, which is trained with simulation data. Once trained, the target of interest can be restored instantly by inputting echo signal into the imaging network, and the simulation experiment also proves that the imaging quality at low SNR superior to state-of-the-art iterative approaches for TCAI.