Facial Image Inpainting Method Based on Attention Mechanism and Multi-scale Feature Aggregation Module
Face image inpainting algorithms based on deep learning often experience information loss when capturing deep features,which could lead to neglection of image semantic features and result in structurally unreasonable inpainting outcomes,hindering texture detail repairs.To address these issues,we proposed an improved face image inpainting network incorporating convolutional block attention module and multi scale feature aggregation module.Firstly,a face image inpaint-ing method based on convolutional block attention module was introduced to enhance the capability of semantic inpainting,ensuring the model generated clear texture inpainting results.Simultaneously,a multi scale feature aggregation module was utilized to capture deep image features and mitigate information loss during convolution processes.Secondly,a CNN encod-er-decoder structure with regularization was designed to mitigate overfitting issues in the inpainting network and enhance its generalization ability.Quantitative experiments conducted on the FFHQ dataset demonstrated that,with a larger mask ra-tio,the peak signal to noise ratio,structural similarity index,and mean absolute error metrics achieved 21.704 2 dB,0.749 2,and 0.041 8 respectively,validating the superiority of the proposed method over existing image inpainting approaches in re-constructing complete information and clear texture details of face images.
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