Example learning is an effective single-image super-resolution reconstruction technique. The key function of this technique is determining how to establish the mapping relationship between high- and low-resolution images. Especially, when dealing with complex and diverse natural images, several studies have shown that it is difficult to reconstruct ideal high-resolution images using a single regression model. Therefore, this study uses the A+ algorithm as a starting point and proposes a super-resolution algorithm based on the theory of Boosting ensemble learning that can adapt to various types of natural images by continuously enhancing the complementarity of the regression model. First, the Boosting scheme is used to train multiple sets of complementary subregressors. Then, all sets of subregressors are merged to generate an integrated model with stronger generalization ability and better reconstruction performance. Finally, a cascaded residual regression strategy and a coarse-to-fine technique are used to gradually synthesize high-resolution images to further improve the image quality. The proposed method was compared with four state-of-the-art examples of learning-based super-resolution methods using five standard datasets. The experimental results show that the proposed method can reconstruct high-quality images with clearer edges and richer texture details.