Terahertz Image Enhancement Based on Generative Adversarial Network

In terahertz scanning imaging, the image contrast is low due to laser power fluctuation and instrument vibration, and the imaging quality needs to be improved. At present, the processing of terahertz image is still in the traditional algorithm stage. In this paper, an image enhancement method based on Generative Adversarial Network is proposed, which includes the idea of deep learning. By introducing blur and noise into the training set image, the mapping relationship between low-quality images and high-quality images is learned and applied to real terahertz images. The experimental results show that, compared with traditional algorithms such as bilateral filtering and non-local mean filtering, this method can significantly improve the image contrast on the basis of improving image details, and has a good visual sense, which provides a new idea for terahertz image enhancement.