Blind face images deblurring with enhancement
Research output: Journal Publications and Reviews (RGC: 21, 22, 62) › 21_Publication in refereed journal › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
Original language | English |
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Pages (from-to) | 2975–2995 |
Journal / Publication | Multimedia Tools and Applications |
Volume | 80 |
Issue number | 2 |
Online published | 18 Sep 2020 |
Publication status | Published - Jan 2021 |
Link(s)
Abstract
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.
Research Area(s)
- Deep learning, Enhancement module, Face images deblurring
Citation Format(s)
Blind face images deblurring with enhancement. / Qi, Qing; Guo, Jichang; Li, Chongyi; Xiao, Lijun.
In: Multimedia Tools and Applications, Vol. 80, No. 2, 01.2021, p. 2975–2995.Research output: Journal Publications and Reviews (RGC: 21, 22, 62) › 21_Publication in refereed journal › peer-review