The learning-based single image super-resolution (SISR) algorithm aims at recovering a high-resolution (HR) image from low-resolution (LR) input. The quality of the HR output mainly depends on the strength of the learning algorithms. Observing that gradient boosting is powerful in dealing with learning problems, we propose a new SISR method based on the gradient boosting framework. First, the boosting framework is extended to the general form of multi-output regression. Then, an error correction approximation is used to sequentially train the boosting trees. The training data for each tree are...
