Blind face images deblurring with enhancement

Face images deblurring has achieved advanced development; however, existing methods involve high computational cost problems. Furthermore, the recovered face images by current methods have the problems of over-smooth textures, ringing artifacts, and poor details. We consider the problem of face images deblurring as a semantic generation task. In this paper, we propose a generative adversarial network (GAN), which includes a perception-inspired blurry removal generator and a discriminator. The proposed generator reconstructs the latent deblurred image by a U-net based network that contains an enhancement module. Face images are highly structured, and thus can be served as a class-specific prior. Considering this, we propose a perceptual loss function to regularize the recovery of face images, which introduces more clear details and reduces the effects of artifacts. The proposed method has a robust capability of generating realistic face images with pleasant visual effects. Extensive experiments on both synthetic and real-world face images demonstrate that the proposed method is comparable with state-of-the-art methods.