A Method for Semantic Image Synthesis with Global Information Enhancement
Semantic image synthesis is an important application and research direction in the field of image translation.Its aim is to generate real images that are consistent with image descriptions using input semantic images,such as semantic segmentation maps,maps and sketches.In response to the problems of blurry image features and lack of correlation in texture details due to the lack of global information in semantic image synthesis tasks based on generative adversarial networks(GANs),this paper proposes a global information-enhanced semantic image synthesis method based on the pix2pix network model,combined with an external attention mechanism.Firstly,an external attention mecha-nism is introduced in the upsampling stage of the generator with a U-net structure to enhance the spatial correlation between generated image pixels.Secondly,deep residual modules are used in the upsampling layers of the generator to improve the quality of generated images while en-hancing the diversity of the generated images.Finally,the discriminator incorporates global information to enhance its discrimination ability.Experimental evaluations on the Cityscape,Landscape,and Edges2shoes datasets demonstrate the effectiveness of the proposed model.Com-pared to the baseline model,the improved method achieves improvements of 57.37,26.74,and 1.78 in terms of the FID(Fréchet Inception Distance)metric for the Cityscape,Landscape,and Edges2shoes datasets,respectively.The results show that the proposed model can effec-tively utilize global information to enhance the correlation of texture details in generated images and improve the quality of generated images.
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