A TPDCU-Net Algorithm for Facial Image Inpainting
XU Kaili
ZHANG Qian
HE Jian
Abstract：To address the existing issues in current algorithms related to the processing of details,texture clarity,and coherence of semantic features,an algorithm for facial image inpainting based on Transformer partial convolution and dilated convolution U-shaped network(TPDCU-Net)was proposed.In the TPDCU-Net network,standard convolutions in the attention mechanism were replaced with partial convolutions to preserve more reliable information and reduce computational load.Simultaneously,in the downsampling process,dilated convolution modules were introduced to minimize the loss of important information,thereby improving the restoration effectiveness.Experimental evaluations on the celebfaces attributes high quality(CelebA-HQ)dataset compared the proposed algorithm with existing image inpainting algorithms using metrics such as peak signal-to-noise ratio(PSNR),structural similarity index measure(SSIM),and mean absolute error(MAE).The results showed that the corresponding metric values were 23.0493dB,0.7786,and 0.0368,respectively when the mask ratio was maximized.The research indicated that the proposed improvement algorithm achieved better results in facial image inpainting tasks.
Keywords：image inpaintingpartial convolutionTransformerdilated convolutionattention mechanismU-Netfacial image
Publication Date：2024-03-20
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：7( 105-110,150 )