Two-stage Patch-based Multi-View Face Superresolution
In this paper, we propose a learning-based method to generate a high-resolution (HR) face in frontal view from a low-resolution (LR) face in an arbitrary pose. This HR virtual face (HRVF) method is based on two stages of pixel-structure learning. In the first stage of our algorithm, initially estimated HR frontal-view images are generated from non-frontal-view LR input images, based on a patch-based learning method. In the second stage, the estimated frontal-view image will be used to search for similar faces from the interpolated LR frontal-view face database. The targeted HR frontal-view face image is then constructed based on the local patches of the HR faces of the corresponding LR face images in the database. Experiments show that the proposed algorithm can produce a better performance than existing methods.
Zhuo Hui Kin-Man Lam
Centre for Signal Processing, Department of Electronic and Information Engineering, The Hong Kong Po Centre for Signal Processing, Department of Electronic and Information Engineering,The Hong Kong Pol
国际会议
2011亚太信号与信息处理协会年度峰会(APSIPAASC 2011)
西安
英文
1-4
2011-10-18（万方平台首次上网日期，不代表论文的发表时间）