Learning stacking regressors for single image super-resolution

Abstract

Example learning-based single image super-resolution (SR) technique has been widely recognized for its effectiveness in restoring a high-resolution (HR) image with finer details from a given low-resolution (LR) input. However, most popular approaches only choose one type of image features to learn the mapping relationship between LR and HR images, making it difficult to fit into the diversity of different natural images. In this paper, we propose a novel stacking learning-based SR framework by extracting both the gradient features and the texture features of images simultaneously to train two complementary models. Since the gradient features are helpful to represent the edge structures while the texture features are beneficial to restore the texture details, the newly proposed method cleverly combines the merits of two complementary features and makes the resultant HR images more faithful to their original counterparts. Moreover, we enhance the SR capacity by using a residual cascaded scheme to further reduce the gap between the super-resolved images and the corresponding original images. Experimental results carried out on seven benchmark datasets indicate that the proposed SR framework performs better than other seven state-of-the-art SR methods in both quantitative and qualitative quality assessments.