Image intelligent translation system based on adaptive multi-domain translation model
Jing Zhang, Zhu Feng · IET conference proceedings. · 2026
Intelligent algorithms have been increasingly used in the field of image translation, especially in tasks such as style migra tion and cross-modal generation, which show strong potential. However, existing image translation methods generally suffer from poor multi-domain adaptability, unstable style migration and lack of precise control over fine-grained semantics. To this end, the research proposes an image translation method based on an adaptive multi-domain translation model, which improves the quality and stability of multi-objective-domain image translation by introducing a priori information to guide content feature extraction and combining the convolutional attention mechanism to enhance the perception of local details. In experimental tests, the research method achieves a structural similarity index of 0.95 and a style consistency score of 0.94 in 100 rounds of training, which is significantly better than Cycle-Consistent Generative Adversarial Network (CycleGAN) and traditional Generative Adversarial Network (GAN), and the model maintains a high accuracy of 0.76 with minimal performance degradation under multi-target domain. Besides, the loss value of the research method under the five-domain scenario is only 0.39, which is significantly better than 0.65 of traditional GAN and 0.58 of CycleGAN, and the accuracy degradation is smaller. This proves that the research method can not only effectively improve the quality of image generation, but also ensure efficient adaptation and strong stability in multi-domain tasks, which has a wide range of application prospects.