Text-to-image synthesis method based on feature-enhanced generative adversarial network

To address the problem of insufficient utilization of image visual features and channel feature information in the process of text-to-image synthesis task, a text-to-image synthesis method based on Feature-enhanced Generative Adversarial Network (FE-GAN) was proposed. Firstly, a Memory on Memory (MoM) module was designed to pay attention to and fuse the generated intermediate features during dynamic memory reading. The attention mechanism was used to enhance the first visual features when memory was read, and then the obtained attention results were fused with the image features generated by the previous generator to achieve the second image visual feature enhancement. Then, channel attention was introduced into the residual block to obtain different semantics in image features, enhance the correlation between similar semantic channels, and achieve channel feature enhancement. Finally, the Instance Normalization Upsampling Block and the Batch Normalization Upsampling Block were combined to improve the image resolution, while mitigating the influence of the batch size on the generation effect and improving the style diversity ability of the generated image. Simulation experiments showed that the Inception Score (IS) of the proposed method reaches 4.83 and 4.13 respectively on the datasets of Caltech-UCSD Birds-200-2011 (CUB-200-2011) and 102 category flower dataset (Oxford-102), which are 1.68% and 5.62% higher than those of DM-GAN, respectively. Experimental results show that the images generated by FE-GAN are better in detail processing and more consistent with text semantics.