Detail Preserving Image Generation Method Based on Semantic Consistency

Abstract

Generative adversarial network is widely used in the field of image generation, but it is easy to lose some image details in the process of image generation. In this paper a detail preserving image generation method based on semantic consistency is proposed to generate fine-grained images that contain more detailed features and improve the semantic consistency of image-text. Firstly, in order to fully explore the potential semantics in text description, feature extraction module is introduced to select important words and sentences, and extract semantic structure feature information between words and sentences. Secondly, the detail preserving module combined with attention mechanism is used to associate the image with the text in-formation, and effectively selects the regions corresponding to the given text. Finally, semantic loss and perceptual loss are utilized to optimize the image-text consistency at the word level and reduce the randomness of image generation. The experimental results show that the IS and FID indexes reach 4.77 and 15.47 on CUB dataset, and 35.56 and 27.63 on COCO dataset, respectively.