现代电子技术2024，Vol.47Issue(9)：40-46,7.DOI:10.16652/j.issn.1004-373x.2024.09.008
基于局部和全局特征融合的二阶段人脸图像修复算法研究
Research on two-stage face image restoration algorithm based on local and global feature fusion
徐克1
作者信息
- 1. 山西大学 物理电子工程学院,山西 太原 030006
- 折叠
摘要
Abstract
A two-stage face image restoration algorithm based on feature fusion and multiscale attention mechanism is proposed to address the artifacts and incoherence that occur during the restoration of large irregularly broken face images.Global and local feature branches are added to the rough repair network to process the output of the encoder.Among them,multi-scale dilated convolution and gated residual concatenation are used to aggregate contextual information of the local feature branch,and then the information is orthogonally fused with the output of the global feature branch to improve the correlation between local and global features and reduce the feature redundancy.The average and maximum pyramid pooling modules are added to the fine repair network,among which the average pooling module is used to capture the overall statistical information,and the maximum pooling module is used to extract spatially salient features and retain the key information.In addition,the convolutional block attention module(CBAM)is used for image feature restructuring and texture generation.A composite function including multi-scale structural similarity loss is constructed to train the network.Experimental results show that the proposed algorithm outperforms the existing algorithms in both subjective and objective evaluation indicators.关键词
全局特征/局部特征/正交融合/金字塔池化/CBAM/多尺度特征融合/人脸图像修复Key words
global feature/local feature/orthogonal fusion/pyramid pooling/CBAM/multi-scale feature fusion/face image inpainting分类
电子信息工程引用本文复制引用
徐克..基于局部和全局特征融合的二阶段人脸图像修复算法研究[J].现代电子技术,2024,47(9):40-46,7.
