Abstract
Position-patch based face hallucination methods aim to reconstruct the high-resolution (HR) patch of each low-resolution (LR) input patch independently by the optimal linear combination of the training patches at the same position. Most of current approaches directly use the reconstruction weights learned from LR training set to generate HR face images, without considering the structure difference between LR and the HR feature space. However, it is reasonable to assume that utilizing HR images for weights learning would benefit the reconstruction process, because HR feature space generally contains much more information. Therefore, in this paper, we propose a novel representation scheme, called High-resolution Reconstructed-weights Representation (HRR), that allows us to improve an intermediate HR image into a more accurate one. Here the HR reconstruction weights can be effectively obtained by solving a least square problem. Our evaluations on publicly available face databases demonstrate favorable performance compared to the previous position-patch based methods.
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Acknowledgements
This work was supported in part by the National Natural Science Foundation of China under Grant No.61273273, 61175096 and 61271374, the Specialized Fund for Joint Bulding Program of Beijing Municipal Education Commission, and the Research Fund for Doctoral Program of Higher Education of China under Grant No. 20121101110043.
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Wan, D., Lu, Y., Ikram, J., Li, J. (2015). Position-Patch Based Face Hallucination via High-Resolution Reconstructed-Weights Representation. In: Ho, YS., Sang, J., Ro, Y., Kim, J., Wu, F. (eds) Advances in Multimedia Information Processing -- PCM 2015. PCM 2015. Lecture Notes in Computer Science(), vol 9314. Springer, Cham. https://doi.org/10.1007/978-3-319-24075-6_41
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DOI: https://doi.org/10.1007/978-3-319-24075-6_41
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