Combining feature fusion and attention mechanism for face image restoration

Jiangtao Liu; Yan Wei; Jinzhi Deng

doi:10.1117/12.2671240

28 April 2023 Combining feature fusion and attention mechanism for face image restoration

Jiangtao Liu, Yan Wei, Jinzhi Deng

Proceedings Volume 12610, Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022); 1261022 (2023) https://doi.org/10.1117/12.2671240
Event: Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022), 2022, Wuhan, China

Abstract

We propose a face image restoration method that combines feature fusion and attention mechanisms for the current image restoration field that generates blurred images, artifacts, inconsistent texture and structure fusion. The model divides image restoration into two stages. First, the edge information repaired by the edge generation adversarial network is used as the prior knowledge of the image, and then the generated prior knowledge and the broken image are put into the image repair network to generate the complete image. We introduce a texture-structure feature fusion method in the generator structure to solve the texture and structure fusion inconsistency problem and use a dense residual layer-hopping connection to mitigate the gradient disappearance problem while speeding up the model convergence and introduce a spatial and channel attention mechanism to generate correct semantic connections to enhance the model performance and suppress image blurring. We apply the algorithm to the CelebA-HQ face dataset, and compared with the current mainstream restoration algorithms, quantitative analysis shows that the method in this paper outperforms in three metrics, PSNR, SSIM, and L1.

Citation Download Citation

Jiangtao Liu, Yan Wei, and Jinzhi Deng "Combining feature fusion and attention mechanism for face image restoration", Proc. SPIE 12610, Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022), 1261022 (28 April 2023); https://doi.org/10.1117/12.2671240

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KEYWORDS

Image restoration

Feature fusion

Semantics

Image fusion

Performance modeling

Convolution

Education and training

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