Abstract:To address the problems of the existing deep-learning defogging algorithm such as the various parameters, long training time, and inability to apply to real-time computer vision systems, this study proposes a bright and dark channel CycleGAN network (BDCCN). BDCCN, based on the CycleGAN, improves the cyclic perceptual loss and achieves image defogging by combining the fixed parameters with training parameters and drawing on the priori theory of bright and dark channels. The experimental results show that the algorithm proposed in this paper, with a small amount of calculation and a fast convergence rate, performs well on both synthetic data sets and real data sets.