A multi-task approach to face deblurring

Image deblurring is a foundational problem with numerous application, and the face deblurring subject is one of the most interesting branches. We propose a convolutional neural network (CNN)-based architecture that embraces multi-scale deep features. In this paper, we address the deblurring problems with transfer learning via a multi-task embedding network; the proposed method is effective at restoring more implicit and explicit structures from the blur images. In addition, by introducing perceptual features in the deblurring process and adopting a generative adversarial network, we develop a new method to deblur the face images with reservation of more facial features and details. Extensive experiments compared with state-of-the-art deblurring algorithms demonstrate the effectiveness of the proposed approach.