Article ID Journal Published Year Pages File Type
562922 Signal Processing 2014 11 Pages PDF
Abstract

•A new face super-resolution (SR) method using 2D CCA is presented.•The method works directly on the 2D image without reshaping the image into vector.•A detail compensation step further enhances the super-resolved face images.•Experimental results show that our method outperforms current SR methods.•The proposed method is computationally efficient due to small matrices involved.

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.

Related Topics
Physical Sciences and Engineering Computer Science Signal Processing
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