Deep Feature Prior Guided Face Deblurring

Abstract

Most recent face deblurring methods have focused on utilizing facial shape priors such as face landmarks and parsing maps. While these priors can provide facial geometric cues effectively, they are insufficient to contain local texture details that act as important clues to solve face deblurring problem. To deal with this, we focus on estimating the deep features of pre-trained face recognition networks (e.g., VGGFace network) that include rich information about sharp faces as a prior, and adopt a generative adversarial network (GAN) to learn it. To this end, we propose a deep feature prior guided network (DFPGnet) that restores facial details using the estimated the deep feature prior from a blurred image. In our DFPGnet, the generator is divided into two streams including prior estimation and deblurring streams. Since the estimated deep features of the prior estimation stream are learned from the VGGFace network which is trained for face recognition not for deblurring, we need to alleviate the discrepancy of feature distributions between the two streams. Therefore, we present feature transform modules at the connecting points of the two streams. In addition, we propose a channel-attention feature discriminator and prior loss, which encourages the generator to focus on more important channels for deblurring among the deep feature prior during training. Experimental results show that our method achieves state-of-the-art performance both qualitatively and quantitatively.