Abstract
The task of face hallucination is to estimate one high-resolution (HR) face image from the given low-resolution (LR) one through the learning based approach. In this paper, a novel local regression learning based face hallucination is proposed. The proposed framework has two phases. In the training phase, after the training samples is separated into several clusters at each face position, the Partial Least Squares (PLS) method is used to project the original space onto a uniform manifold feature space and multiple linear regression are learned in each cluster. In the prediction phase, once the cluster of the LR patch is gotten, the corresponding learned regression function can be used to estimate HR patch. Furthermore, a multi-regressors fusion model and HR induced clustering strategy are proposed to further improve the reconstruction quality. Experiment results show that the proposed method has a very competitive performance compared with other leading algorithm with low complexity.
This research was supported in part by the National Nature Science Foundation, P.R. China (No. 61071166, 6172118, 61071091, 61471201), Jiangsu Province Universities Natural Science Research Key Grant Project (No. 13KJA510004), and the “1311” Talent Plan of NUPT.
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Jiao, C., Gan, Z., Qi, L., Chen, C., Liu, F. (2016). Novel Face Hallucination Through Patch Position Based Multiple Regressors Fusion. In: Tan, T., Li, X., Chen, X., Zhou, J., Yang, J., Cheng, H. (eds) Pattern Recognition. CCPR 2016. Communications in Computer and Information Science, vol 662. Springer, Singapore. https://doi.org/10.1007/978-981-10-3002-4_31
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DOI: https://doi.org/10.1007/978-981-10-3002-4_31
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