Multi-period Infrared Image Generation Based on Multi-conditional Cycle Generative Adversarial Networks

Since the number of most infrared image samples is too small, many deep learning networks don’t work well on these infrared datasets. In this paper, we propose a Multi-conditional Cycle Generative Adversarial Network, which can derive multi-time infrared images from some fixed-time infrared images, and can be used to augment the small infrared image dataset. By taking several different time periods as multi-condition constraints, the model we proposed, which is based on generative adversarial network (GAN), can derive infrared images in different time periods. In order to improve the quality of derived infrared images, this paper introduces L1 norm and Wasserstein distance to construct the objective function, which make it easier to stabilize the training of the network, and the model can learn more useful features. The small infrared images dataset in our experiment is built by our team own. The experiments show that the method proposed in this paper can derive multi-period infrared images. Compared with the CGAN, our model can learn the feature mapping between the original images and the derived images more accurately, and the generated infrared images are richer in details and more realistic by vision.