A novel face super resolution approach for noisy images using contour feature and standard deviation prior
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- 作者：Liang Chen ; Ruimin Hu ; Chao Liang ; Qing Li ; Zhen Han
- 关键词：Face super resolution ; Joint learning ; Noise scenario ; Contour feature ; Facial standard deviation
- 刊名：Multimedia Tools and Applications
- 出版年：2017
- 出版时间：January 2017
- 年：2017
- 卷：76
- 期：2
- 页码：2467-2493
- 全文大小：
- 刊物类别：Computer Science
- 刊物主题：Multimedia Information Systems; Computer Communication Networks; Data Structures, Cryptology and Information Theory; Special Purpose and Application-Based Systems;
- 出版者：Springer US
- ISSN：1573-7721
- 卷排序：76
文摘
Face Super Resolution (FSR) is to infer high resolution (HR) face images from given low resolution (LR) face images with the help of HR/LR training examples. The most representative FSR is NE methods, which are based on the consistency assumption that the HR/LR patch pairs form similar local geometric structures. But NE methods have difficulty in dealing with noisy facial images. The reason lies in the wrong neighborhood relationship caused by low quality scenarios that even two distinct patches have similar relation in local geometry. Therefore, the consistency assumption is not well held anymore. This paper presents a novel FSR approach suitable for noisy facial images. Our work are twofold. Firstly, different from the existing methods which directly enhance the noisy input image in intensity feature space, the proposed method introduces a contour feature which is robust to noise. By applying the contour feature as constraint, the noise effects can be effectively suppressed. Secondly, different from the existing methods which directly constrain the noisy input image with low quality contour feature, a standard deviation prior is proposed to enhance the low quality contour feature. Through enhancing the contour feature into high quality, the FSR reconstruction can be better constrained. Both simulation and the real-world scenario experiments demonstrate that the proposed approach outperforms most classic methods both quantitatively and qualitatively.