example learning-based image super-resolution techniques estimate a high-resolution image from a low-resolution input image by relying on high-and low-resolution image pairs. an important issue for these techniques is how to model the relationship between high-and low-resolution image patches: most existing complex models either generalize hard to diverse natural images or require a lot of time for model training, while simple models have limited representation capability. in this paper, we propose a simple, effective, robust, and fast (serf) image superresolver for image super-resolution. the proposed super-resolver is based on a series of linear least squares functions, namely, cascaded linear regression. it has few parameters to control the model and is thus able to robustly adapt to different image data sets and experimental settings. the linear least square functions lead to closed form solutions and therefore achieve computationally efficient implementations. to effectively decrease these gaps, we group image patches into clusters via k-means algorithm and learn a linear regressor for each cluster at each iteration. the cascaded learning process gradually decreases the gap of highfrequency detail between the estimated high-resolution image patch and the ground truth image patch and simultaneously obtains the linear regression parameters. experimental results show that the proposed method achieves superior performance with lower time consumption than the state-of-the-art methods.