Learning-based Super-resolution via Canonical Correlation Analysis

The task of image super-resolution is to up sample a low resolution (LR) image while recovering sharp edges and high frequency details. In this paper, a single image superresolution algorithm via canonical correlation analysis (CCA) is proposed. This method is based on the assumption that the corresponding LR and high resolution (HR) images have high correlation coefficients when transformed into a special space. The proposed approach includes two stages: training and testing. In the training stage, a couple of canonical bases for transformation are calculated with the prepared coupled training sets. In the testing stage, the HR image can be recovered by using the canonical bases obtained in the training stage. In addition, an iterative back projection algorithm is used to further improve the image quality. The experiments demonstrate that our algorithm can reconstruct richer details, with fewer artifacts. Moreover, this algorithm is of less complexity.