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.
Bibliographical noteFunding Information:
This work has been supported in part by the NSF CAREER (No. 1149783), NSF of China (No. 61872421 and 61572099), NSF of Jiangsu Province (No. BK20180471), and National Science and Technology Major Project (2018ZX04041001-007).
Acknowledgements This work has been supported in part by the NSF CAREER (No. 1149783), NSF of China (No. 61872421 and 61572099), NSF of Jiangsu Province (No. BK20180471), and National Science and Technology Major Project (2018ZX04041001-007).
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All Science Journal Classification (ASJC) codes
- Computer Vision and Pattern Recognition
- Artificial Intelligence