# Fast Terahertz Coded-Aperture Imaging Based on Convolutional Neural Network
## 3 Results
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Center frequency \(f_{c}\) & 340 GHz \\ Bandwidth & 20 GHz \\ Imaging distance & 2 m \\ Size of coded aperture \(\Delta b\) & 0.3 m \(\times\) 0.3 m \\ Size of imaging plane \(\Delta s\) & 0.3 m \(\times\) 0.3 m \\ Number of time sampling \(N\) & 3600 \\ Number of coded aperture elements & \(60\times 60\) \\ Number of grid cells in the imaging plane & \(60\times 60\) \\ The distance between coded aperture and receiver & 0.15 m \\ The distance between coded aperture and transmitter & 1 m \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameters Used in the TCAI Simulation Experiment. intensity more authentically and the target outline is clearer. For TVAL3 algorithm, the target can be perfectly reconstructed when the SNR is larger than 15 dB. However, its performance degraded dramatically as SNR go lower. In general, our performance is competitive with the typical optimization iterative algorithms when SNR is not larger than 10 dB. It is main reason that CS-based algorithms are not quite robust since some errors exist in the reference signal matrix \(\mathbf{S}\). Nonetheless, the developed network can automatically fix these errors during the training. With the increase of SNR, we also note that the imaging performance of the proposed method is ultimately bounded by the imaging network's representational error. However, as can be seen in the predicted target, the reconstructions are semantically correct. Quantitatively, the MSEs of different methods under different SNRs are calculated and the results are drawn in Figure 5. For each SNR, the shape of the target changes randomly in each trial. As expected, the SBL algorithm is worse than the proposed method no matter for which SNR. Although the TVAL3 algorithm shows good performance in high SNR, but it is sensitive to noise and takes more time than the presented algorithm. Table 2 shows the time cost of various algorithms, each of which is the average of 100 trials. Due to the neural network-based approach can be easily parallelized, we also recorded the reconstruction time of the presented method with GPU implementation. It is easy to calculate that the imaging frame rate of our method is no less than 280 Hz. From these results, we can see that the proposed algorithm has great superiority with imaging efficiency. One explanation is that an end-to-end network can directly transform the echo signal into the target, while classic imaging techniques require a large amount of iterations to estimate a satisfactory solution. Therefore, it is unsurprising that the neural network based method is much less time consuming. Thus, experimental results above fully illustrate that the proposed method is a promising tool for fast TCAI under low SNR. More evidence is shown in Figure 6. Again, we can see the superiority of deep learning-based approach clearly. Figure 4: Comparison of reconstruction results from various methods at different signal to noise ratio (SNR). Figure 3: Reconstruction results for different ratio of T/M based on the proposed algorithm.