Face image completion is an important image processing technique for reconstructing face images in the field of computer vision. The existing face image completion methods have the problem of unreasonable global semantics

which is mainly due to the lack of long-range transfer capability of the existing techniques that they are unable to reasonably transfer information from known regions in a broken image to occluded regions. To overcome the problem

a novel encoder-decoder face image completion network integrating style-aware and multi-scale attention was proposed under the framework of generative adversarial network (GAN). Specifically

the style-aware module was used to extract the global semantic information of an image

and the extracted information was employed to globally adjust the completion processing by rendering the encoding of the image level by level. The multi-scale attention module extracted patches of multi-scale features and performed a long-range transfer via matrix multiplication between a shared attention score and the extracted patches. Experimental results from the public dataset CelebA-HQ show that the style-aware module and the multi-scale attention module greatly enhanced the long-range transfer capability of the completion network. Compared with the existing state-of-the-art face image completion methods

the proposed model had significant improvement in various evaluation metrics. Meanwhile

the global semantics of the completion results were more reasonable and the completion effect was more natural under low lighting conditions.