Single-Image Super-Resolution Using Active-Sampling Gaussian Process Regression

As well known, Gaussian process regression (GPR) has been successfully applied to example learning-based image super-resolution (SR). Despite its effectiveness, the applicability of a GPR model is limited by its remarkably computational cost when a large number of examples are available to a learning task. For this purpose, we alleviate this problem of the GPR-based SR and propose a novel example learning-based SR method, called active-sampling GPR (AGPR). The newly proposed approach employs an active learning strategy to heuristically select more informative samples for training the regression parameters of the GPR model, which shows significant improvement on computational efficiency while keeping higher quality of reconstructed image. Finally, we suggest an accelerating scheme to further reduce the time complexity of the proposed AGPR-based SR by using a pre-learned projection matrix. We objectively and subjectively demonstrate that the proposed method is superior to other competitors for producing much sharper edges and finer details.