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• W1987017523 abstract "In this paper a face super-resolution method using two-dimensional canonical correlation analysis (2D CCA) is presented. A detail compensation step is followed to add high-frequency components to the reconstructed high-resolution face. Unlike most of the previous researches on face super-resolution algorithms that first transform the images into vectors, in our approach the relationship between the high-resolution and the low-resolution face image are maintained in their original 2D representation. In addition, rather than approximating the entire face, different parts of a face image are super-resolved separately to better preserve the local structure. The proposed method is compared with various state-of-the-art super-resolution algorithms using multiple evaluation criteria including face recognition performance. Results on publicly available datasets show that the proposed method super-resolves high quality face images which are very close to the ground-truth and performance gain is not dataset dependent. The method is very efficient in both the training and testing phases compared to the other approaches." @default.
• W1987017523 created "2016-06-24" @default.
• W1987017523 creator A5005639972 @default.
• W1987017523 creator A5071522801 @default.
• W1987017523 date "2014-10-01" @default.
• W1987017523 modified "2023-09-27" @default.
• W1987017523 title "Face image super-resolution using 2D CCA" @default.
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• W1987017523 doi "https://doi.org/10.1016/j.sigpro.2013.10.004" @default.
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