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International Journal of Computer Vision
Erschienen in: International Journal of Computer Vision 6-7/2019

17.01.2019

Joint Face Hallucination and Deblurring via Structure Generation and Detail Enhancement

verfasst von: Yibing Song, Jiawei Zhang, Lijun Gong, Shengfeng He, Linchao Bao, Jinshan Pan, Qingxiong Yang, Ming-Hsuan Yang

Erschienen in: International Journal of Computer Vision | Ausgabe 6-7/2019

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Abstract

We address the problem of restoring a high-resolution face image from a blurry low-resolution input. This problem is difficult as super-resolution and deblurring need to be tackled simultaneously. Moreover, existing algorithms cannot handle face images well as low-resolution face images do not have much texture which is especially critical for deblurring. In this paper, we propose an effective algorithm by utilizing the domain-specific knowledge of human faces to recover high-quality faces. We first propose a facial component guided deep Convolutional Neural Network (CNN) to restore a coarse face image, which is denoted as the base image where the facial component is automatically generated from the input face image. However, the CNN based method cannot handle image details well. We further develop a novel exemplar-based detail enhancement algorithm via facial component matching. Extensive experiments show that the proposed method outperforms the state-of-the-art algorithms both quantitatively and qualitatively.
Vorheriger Artikel Wavelet Domain Generative Adversarial Network for Multi-scale Face Hallucination
Nächster Artikel Motion Deblurring of Faces

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Metadaten
Titel
Joint Face Hallucination and Deblurring via Structure Generation and Detail Enhancement
verfasst von
Yibing Song
Jiawei Zhang
Lijun Gong
Shengfeng He
Linchao Bao
Jinshan Pan
Qingxiong Yang
Ming-Hsuan Yang
Publikationsdatum
17.01.2019
Verlag
Springer US
Erschienen in
International Journal of Computer Vision / Ausgabe 6-7/2019
Print ISSN: 0920-5691
Elektronische ISSN: 1573-1405
DOI
https://doi.org/10.1007/s11263-019-01148-6

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