Learning sparse image representation with support vector regression for single-image super-resolution
Learning-based approaches for super-resolution (SR) have been studied in the past few years. In this paper, a novel single-image SR framework based on the learning of sparse image representation with support vector regression (SVR) is presented. SVR is known to offer excellent generalization ability in predicting output class labels for input data. Given a low resolution image, we approach the SR problem as the estimation of pixel labels in its high resolution version. The feature considered in this work is the sparse representation of different types of image patches. Prior studies have shown that this feature is robust to noise and occlusions present in image data. Experimental results show that our method is quantitatively more effective than prior work using bicubic interpolation or SVR methods, and our computation time is significantly less than that of existing SVR-based methods due to the use of sparse image representations.