A FACE IMAGE INPAINTING ALGORITHM BASED ON SELF ATTENTION MECHANISM
In the application of current deep learning methods in large area information missing face image inpainting,the inpainting results show issues such as blurred texture details,structural deformation,and distortion.Aimed at these problems,an image inpainting algorithm based on self-attention mechanism is proposed.The image to be completed was input into the rough generation network based on skip-connection to get the preliminary repair.The initial results were input into the self-attention sensing branch and the hybrid hole convolution branch to encode together,and the generated results were obtained by decoding.The dual discriminant was used to optimize the discriminant.Through the experiments and tests on face image CelebA-HQ dataset,the results show that the proposed method has better inpainting effect than the deep fill and PLC algorithms in objective and subjective evaluation.
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