I-GANs for Synthetical Infrared Images Generation

Abstract

Due to the insensitivity of infrared images to changes in light intensity and weather conditions, these images are used in many surveillance systems and different fields. However, despite all the applications and benefits of these images, not enough data is available in many applications due to the high cost, time-consuming, and complicated data preparation. Two deep neural networks based on Conditional Generative Adversarial Networks are introduced to solve this problem and produce synthetical infrared images. One of these models is only for problems where the pair to pair visible and infrared images are available, and as a result, the mapping between these two domains will be learned. Given that in many of the problems we face unpaired data, another network is proposed in which the goal is to obtain a mapping from visible to infrared images so that the distribution of synthetical infrared images is indistinguishable from the real ones. Two publicly available datasets have been used to train and test the proposed models. Results properly demonstrate that the evaluation of the proposed system in regard to peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) has improved by 4.6199% and 3.9196%, respectively, compared to previous models.