Unsupervised Terahertz Image Restoration Based on CycleGan

Abstract

Abstract Terahertz (THz) is considered as one of the key technologies for sixth generation communications, military, medical imaging and industrial inspection. THz images are susceptible to degradation due to system noise and point spread functions during transmission. The existing deep learning methods use ground truth and input images for supervised training that can recover THz images very well. But it’s difficult to obtain labeled THz data in practical application. In this paper, we propose an attentional adversarial cycle generation network for THz image restoration (CycleTHz) based on CycleGan to address this problem. The CycleTHz generates clean images firstly by an attention-guided generation network and then discriminates the quality of the generators by an attention discriminator. In addition, RGB color loss is used for image channels for constraint. To the best of our knowledge, this is the first THz dataset to be trained using an unsupervised approach. Extensive experiments show that the proposed method improves the PSNR and SSIM by 43.4% and 101.7% compared with CycleGan, which is a benchmark method for the unsupervised development in THz image restoration. The code is available at https://github.com/hellogry/UnsupervisedCycleTHz