Multi-Domain Image Translation Adversarial Network Based on Artificial Intelligence

Traditional image translation methods often face difficulties such as differences in cross domain data distribution and difficulty in maintaining semantic consistency. Therefore, this article cites a generative adversarial network (GAN) based approach to capture mapping relationships between different domains. Firstly, this article adopts the Conditional Generative Adversarial Network (cGAN) framework to generate multi domain images. Next, this article introduces a shared encoder and multiple decoder structure. Finally, this article improved the performance of image translation results through optimization. In the experimental results, our method performed well on multiple standard datasets. The SSIM (Structural Similarity Index) value for the ink painting to oil painting task remains stable at around 0.88, and the PSNR (Peak Signal to Noise Ratio) reaches 32.5 dB. In terms of diversity generation, this method also significantly improves the diversity and visual naturalness of image translation. Finally, the method proposed in this article significantly improves the quality and diversity of style transfer.