Abstract
Local linear embedding (LLE) is a promising manifold learning method in the field of machine learning. Number of face hallucination (FH) methods have been proposed due to its neighborhood preserving nature. However, the projection of low resolution (LR) image to high resolution (HR) is “one-to-multiple” mapping; therefore manifold assumption does not hold well. To solve the above inconsistency problem we proposed a new approach. First, an intermediate HR patch is constructed based on the non linear relationship between LR and HR patches, which is established using partial least square (PLS) method. Secondly, we incorporate the correlative residue compensation to the intermediate HR results by using only the HR residue manifold. We use the same combination coefficient as for the intermediate hallucination of the first phase. Extensive experiments show that the proposed method outperforms some state-of-the-art methods in both reconstruction error and visual quality.
Nie Hui—This work was supported in part by the National Natural Science Foundation of China under Grant Nos. 61273273 and 61271374, and by Research Fund for the Doctoral Program of Higher Education of China under Grant No. 20121101110034.
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Ikram, J., Lu, Y., Li, J., Hui, N. (2016). Face Hallucination Using Correlative Residue Compensation in a Modified Feature Space. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds) Neural Information Processing. ICONIP 2016. Lecture Notes in Computer Science(), vol 9948. Springer, Cham. https://doi.org/10.1007/978-3-319-46672-9_12
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