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- Title
A multi-task approach to face deblurring.
- Authors
Shen, Ziyi; Xu, Tingfa; Zhang, Jizhou; Guo, Jie; Jiang, Shenwang
- Abstract
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.
- Subjects
HUMAN facial recognition software; ARTIFICIAL neural networks; IMAGE quality analysis; MULTISCALE modeling; MACHINE learning; IMAGE reconstruction
- Publication
EURASIP Journal on Wireless Communications & Networking, 2019, Vol 2019, Issue 1, p1
- ISSN
1687-1472
- Publication type
Article
- DOI
10.1186/s13638-019-1350-3