Face image super-resolution using 2D CCA

• 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.