Abstract
Blurry textures in the inpainted regions or content inconsistencies between the inpainted and known regions may arise with existing deep-learning based completion approaches. A two-stage face completion framework with perceptual deblurring to address these problems is presented. In the first stage, a GAN is employed to inpaint masked regions with coarse facial features by integrating PSNR loss, SSIM loss and adversarial loss. In the second stage, a perceptual deblurring GAN is developed to further refine the coarsely reconstructed facial features in order to achieve clear facial textures and natural content consistencies. The quantitative, qualitative and ablation experiments are performed on the public face dataset CelebAHQ. Extensive experimental results show that the proposed algorithm is superior to the compared state-of-the-art image completion algorithms on the metrics of PSNR and SSIM.
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