CFA-GAN: Cross fusion attention and frequency loss for image style transfer

Abstract

Image style transfer is a significant challenge in computer vision, where the goal is to transfer the style of a reference image to a source image while preserving its background and feature information. Despite previous progress in this area, many studies have overlooked the issues of local details and complexity, leading to unrealistic generated images and slow model inference speeds. This paper proposes an efficient image style transfer method based on cross-fusion attention (CFA), inspired by generative adversarial networks (GAN) and a new joint loss. Our novel CFA encodes local contextual information between low-level and high-level features, models remote dependencies of features, and embeds CFA into the encoder of our proposed model to increase the diversity of small features and focus on more prominent feature parts. Furthermore, we use frequency domain loss to improve image reconstruction quality and a joint loss to train the network. Our experiments on multiple datasets demonstrate that our proposed method offers significant advantages in terms of inference speed, background information preservation, and local details, making it a promising approach to image style transfer. The code for our method is available at https://github.com/berylxzhang/CFA-GAN.