# A THz Passive Image Generation Method Based on Generative Adversarial Networks
###### Abstract  
A terahertz (THz) passive imager with automatic target detection is an effective solution in the field of security inspection. The high-quality training datasets always play a key role in the high-precision target detection applications. However, due to the difficulty of passive image data acquisition and the lack of public dataset resources, the high-quality training datasets are often insufficient. The generative adversarial network (GAN) is an effective method for data augmentation. To enrich the dataset with the generated images, it is necessary to ensure that the generated images have high quality, good diversity, and correct category information. In this paper, a GAN-based generation model is proposed to generate terahertz passive images. By applying different residual connection structures in the generator and discriminator, the models have strong feature extracting ability. Additionally, the Wasserstein loss function with gradient penalty is used to maintain training stability. The self-developed 0.2 THz band passive imager is used to carry out imaging experiments, and the imaging results are collected as a dataset to verify the proposed method. Finally, a quality evaluation method suitable for THz passive image generation task is proposed, and classification tests are performed on the generated images. The results show that the proposed method can provide high-quality images as supplementary. t 2022202  
The dataset always plays a key role in model training and generalization performance. As we all know, THz security inspection equipment has just begun to be set in a few security inspection scenarios. At present, there is no public dataset available for training models to identify specific dangerous targets, and this dataset involves sensitive issues such as public safety. Therefore, using data augmentation technology to fully mine the existing dataset is a necessary means to improve the performance of the model. Most of the existing data augmentation techniques involve random cropping, splicing, rotation and other operations on the original image during the training process [6; 7]. However, such technologies cannot generate some unseen images and do not fundamentally solve the problem. Especially in the case of few-shot learning, the improvement of network performance is limited. With the continuous progress of deep-learning algorithms, image generation methods based on deep convolutional neural networks provide a solution for data augmentation. The generative adversarial network (GAN) [8] is a typical image generation method. GAN models learn feature distribution of real data and improve the quality of generated images through game learning between the generator and the discriminator. The early GAN models performed feature extraction through the fully connected layer, which ignored the spatial correlation of the image. Thus, the quality of the generated image was poor. Then the deep convolutional network structure was introduced into the GAN model (DCGAN) [9], which improved the quality of generated image. Subsequently, to generate images of multiple categories during one training process, a supervised learning GAN with image classification information is proposed [10]. However, the introduction of classification labels combined with the dynamic training process has a negative impact on the generated image categories [11]. In addition, the abovementioned GAN model uses the Jensen-Shannon (JS) divergence to measure the difference between the generated image and the real image. The JS divergence loss function will be a constant in most cases, resulting in invalid gradient backpropagation and unstable training. Then, [PERSON] et al. used the Wasserstein distance with weight clipping to replace the JS divergence, and proposed the WGAN model [12], which improved the training stability. Then, [PERSON] et al.