Morphological component analysis and least squares support vector machine for image super-resolution

In this paper, a new approach is proposed for image super-resolution by combining morphological component analysis and least squares support vector machines. The proposed approach for image super-resolution consists of three steps. First, under morphological component analysis, the high resolution and low resolution images are individually decomposed into high and low frequency components, respectively. Second, the weights of two least squares support vector machines are trained by the low frequency components of the low/high resolution images and the high frequency component of low/high resolution images, respectively. Finally, the high resolution image is then reconstructed via sum of the predicted outputs from two least squares support vector machines. Experimental results show that the proposed super-resolution method performs better than the traditional bi-cubic interpolation method.