Effective model construction for enhanced prediction in example-based super-resolution

Abstract

Single-image super-resolution is widely adopted for high resolution display related applications. Example learning-based approaches can provide plenty of image details by using trained dataset. Regression-based methods reduce the memory storage size by training mapping functions instead of using a huge dictionary. The reconstructed image quality can be further enhanced by combining various prediction results. This work presents an effective model reconstruction method for enhanced predictions. The desired model can be constructed offline when using the local multi-gradient level pattern as the clustering feature. Applying the proposed schemes can further improve the quality of reconstructed high resolution image while retaining almost the same time complexity as the original solution. Experimental results exhibit that the quality of reconstructed image using the proposed schemes is very close to that of Yang's work, but the proposed one can operate much faster than his solutions. Moreover, the space for storing mapping functions can be dramatically reduced by using the proposed model combining method.