Image Style Transformation Based on structure GAN
Xiao Wu, Yaochen Li, Zelin Hao, Chuan Wu, Xiaochun Wang, Yuehu Liu · 2019
A large amount of datasets are required for the off-line test of unmanned vehicles. Since the redundancy of different image datasets exists, we apply image style transformation to generate new images, which simplifies the data collection process. In this paper, an image-to-image transformation network is proposed based on the structural information. The proposed network consists of two branches, one for style transfer and the other for semantic segmentation. We use the structural information extracted by semantic segmentation to constrain the transformed image, so that the transformed image can preserve the integrity and structural consistency. We conduct the experiments and comparisons with the baseline methods. The experimental results prove that the proposed method can generate more visually pleasant images for the tasks of day-night transformation, sunny-foggy transformation, and summer-winter transformation.