深度融合图像文本特征的文本引导图像修复.

In order to solve the problem that the existing text guided image inpainting models lack efficient fusion of information between modes when dealing with text image fusion, resulting in unreal repair results and poor semantic consistency, this paper proposed a text guided image inpainting model BATF, which integrated image text features through conditional batch normalization. Firstly, it normalized the damaged and undamaged regions respectively by the spatial region normalization encoder to reduce the influence of direct feature normalization on the mean variance shift. Secondly, through the depth affine transformation, it fused the extracted image features and the text feature vectors to enhance the visual semantic embedding of the generator network feature map, so that the image and the features could be fused more effectively. Finally, it designed an efficient discriminator and introduced a target perception discriminator in this paper to enhance the texture authenticity and semantic consistency of the repaired image. Quantitative and qualitative experiments on CUB bird, a text-labeled dataset, show that the proposed model achieves 20.86,0.836,and 23.832 for PSNR, SSIM, and MAE, respectively. BATF model is better than the existing models MMFL and ALMR, and the repaired images both meet the requirements of given text attributes and have high semantic consistency. [ABSTRACT FROM AUTHOR]