UHA‐CycleGAN: Unpaired hybrid attention network based on CycleGAN for terahertz image super‐resolution

Abstract

Abstract In recent years, terahertz imaging technology has been widely used in security, medicine, and other fields. However, the image resolution is low due to the limits of imaging equipment and diffraction. Traditional super‐resolution methods based on machine learning use artificial paired image datasets, and their image degradation process is quite different from the actual terahertz imaging mechanism. In the paper, an unpaired hybrid attention network is proposed, using the real unpaired high‐resolution and low‐resolution terahertz images as the original input, two sub‐networks are trained: degenerate reconstruction network and super‐resolution network. The degenerate reconstruction network is used to learn the degradation process of the real terahertz imaging system, and reconstruct the generated degradation images into the fixed mapping down‐sampled image of high‐resolution terahertz image. The super‐resolution network uses the generated paired image with fixed correspondence to train the super‐resolution network of paired terahertz images, to improve the imaging resolution of the network in the super‐resolution work of real low‐resolution terahertz images. In addition, the no‐reference image quality evaluation system is introduced to objectively evaluate the network performance. The experimental results show that the UHA‐CycleGAN network outperforms the traditional paired super‐resolution network on both open‐source and self‐built datasets.

Keywords

• image resolution
• unsupervised learning