基于感知去模糊的高效人脸图像修复算法

Efficient Generative Face Completion with Perceptual Deblurring

针对现有深度学习图像修复算法在修复区域内部产生模糊纹理或存在修复区域边界的内容不连贯性的问题,提出一种基于感知去模糊的人脸图像修复算法.首先提出一种包含PSNR损失、SSIM损失和对抗损失的生成对抗网络生成粗略的人脸结构修复信息;然后开发一种基于感知去模糊的生成对抗网络对粗略的人脸结构进行进一步的纹理细节恢复,从而生成自然清晰且内容连贯的面部纹理.在CelebAHQ公开人脸数据集上进行定量、定性和消融实验的结果表明,所提算法在峰值信噪比和结构相似性等定性评价指标方面优于所对比的前沿图像修复算法.

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.