To address the problem of texture blurring and structural distortion when repairing large irregular damaged areas in existing facial image repair algorithms, due to their inability to extract dynamic features and lack of edge prior information, this paper proposed a facial image repair algorithm based on edge prior fusion and dynamic gated feature. Firstly, it designed a dynamic gated convolution module to dynamically extract features from the damaged area, linking effective features of the known and missing areas to enhance the delicacy of the texture. Then, it designed a dynamic gated edge enhancement network and a U-shaped encoding texture repair network. The edge enhancement network aimed to capture edge contour information, providing structural prior constraints for the U-shaped encoding texture repair network. The U-shaped encoder texture repair network adopted the UNet++ to fuse multi-layer features to ensure the consistency of structure and texture in the repaired facial image. Finally, it demonstrated the effectiveness and universality of the UNet++through ablation experiments, and pruned U-shaped network to select the appropriate facial image model representation layer for texture reconstruction in the missing area. Experiments were carried out on the CelebA-HQ facial dataset for evaluation. The experimental results show that compared with mainstream algorithms, the proposed method improves SSIM by an average of 3.87%, PSNR by an average of 3.79 dB, and reduces FID by an average of 16.54%, thereby more effectively repairing large areas of irregular missing regions, and generating images with clear texture and reasonable structure.

[2023-07-05] Accepted Paper /doc/2023.03.0123/v1

[2023-11-05] Printed Article /doc/2023.03.0123/v2