Abstract
A learning-based face hallucination method is proposed in this paper for the reconstruction of a high-resolution face image from a low-resolution observation based on a set of high- and low-resolution training image pairs. The proposed global linear modal based super-resolution estimates the optimal weights of all the low-resolution training images and a high-resolution image is obtained by applying the estimated weights to the high-resolution space. Then, we propose a position based local residue compensation algorithm to better recover subtle details of face. Experiments demonstrate that our method has advantage over some established methods.
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© 2009 Springer-Verlag Berlin Heidelberg
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Ma, X., Zhang, J., Qi, C. (2009). Hallucinating Faces: Global Linear Modal Based Super-Resolution and Position Based Residue Compensation. In: Foggia, P., Sansone, C., Vento, M. (eds) Image Analysis and Processing – ICIAP 2009. ICIAP 2009. Lecture Notes in Computer Science, vol 5716. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04146-4_89
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DOI: https://doi.org/10.1007/978-3-642-04146-4_89
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