In the high-resolution image style transfer task of road traffic scenes, how to fully transfer style information while retaining the original content structure information is a challenging problem. In this paper, a novel Transformer-based generative adversarial network for high-resolution un-paired image translation is proposed. Firstly, we design a style transformation module based on cross adaptive Transformer, which dynamically adjusts the content features to achieve statistical alignment between content features and the target style. Meanwhile, an image frequency-domain enhancement module is designed based on cross-attention, which fuses the global information of the low-frequency style with the local details of the high-frequency content information. The detailed texture is then enhanced while the image style consistency is maintained. Furthermore, we design a threshold-guided negative sample screening strategy based on contrastive learning, which can improves the model's transfer effect. The experimental results well demonstrate the effectiveness of the proposed method.

With the advancement of autonomous driving technology, there is an increasing demand for high-quality and diverse images of road traffic scenes. Style transfer techniques can be employed to synthesize large-scale datasets. However, existing image style transfer methods often exhibit suboptimal performance in transferring styles for road scenes, frequently struggling to maintain structural consistency. In this paper, we propose a novel network architecture for unsupervised image style transfer named SVDGNet. This architecture dynamically adjusts the weights of different image regions during model training by calculating the Shapley values for the source and target domain images. We also employ a pre-trained diffusion model to generate better-stylized images. The experimental results demonstrate that the proposed method achieves better performance compared to the existing methods, which can preserve the structural consistency of the source domain images while providing impressive style transfer results.

For the task of unsupervised image translation, transforming the image style while preserving its original structure remains challenging. In this paper, we propose an unsupervised image translation method with structural enhancement in frequency domain named SEIT. Specifically, a frequency dynamic adaptive (FDA) module is designed for image style transformation that can well transfer the image style while maintaining its overall structure by decoupling the image content and style in frequency domain. Moreover, a wavelet-based structure enhancement (WSE) module is proposed to improve the intermediate translation results by matching the high-frequency information, thus enriching the structural details. Furthermore, a multi-scale network architecture is designed to extract the domain-specific information using image-independent encoders for both the source and target domains. The extensive experimental results well demonstrate the effectiveness of the proposed method.