一种改进的CycleGAN图像风格迁移算法

An Improved CycleGAN Image Style Transfer Algorithm

针对目前生成对抗网络的图像风格迁移效果存在细节丢失、图像真实性有待提高等问题,提出了一种改进的CycleGAN网络模型,使用U-Net代替原来的ResNet网络,以更好地保留图像细节和结构;在生成器和判别器中融入自注意力机制,进一步提升对重要细节的关注和重建能力,产生更逼真细腻的迁移效果。消融实验结果表明,该算法有效改善了风格迁移过程中细节丢失等问题,生成图像具有更好的视觉效果和真实感。

In view of the problems such as detail loss and image authenticity need to be improved in the image style transfer effect of the current generation adversarial network,an improved CycleGAN network model is proposed.U-Net is used to replace the original ResNet network,so as to better preserve the image details and structure.The self-attention mechanism is integrated into the generator and discriminator to further improve the attention and reconstruction ability of important details,and produce a more realistic and delicate transfer effect.The ablation experiment results show that the proposed algorithm can effectively improve the detail loss in the process of style transfer,and generate images with better visual effects and real sense.