基于学习的图像超分辨率技术, 通过学习获得高、低分辨率图像之间的映射关系, 将其作为先验约束条件来估计高分辨率图像。这种技术的一个重要问题是如何建立高分辨率和低分辨率图像之间的映射关系, 大多数现有的复杂模型既难以推广到所有自然图像, 还需要耗费大量时间进行模型训练, 而简单模型的表示能力却很有限。本文提出了一种简单、有效、鲁棒、快速的图像超分辨率技术。这种超分辨技术基于一系列线性最小二乘函数, 即级联线性回归模型, 这种模型函数具有闭合形式的解, 仅需要很少的控制参数, 因此在计算上能够有效实现。为了减小估计模型和实际模型之间的差距, 本文通过 k-means算法将图像块进行聚类, 并在每次迭代中学习每个聚类的线性回归参数, 在级联线性回归学习过程中逐渐逼近真实的超分辨率图像。实验结果表明, 本文所提出的技术与现有技术方法相比, 具有更好的超分辨性能、更低的时间消耗。

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 the high- and low-resolution image patches: most exist-ing complex models either generalize hard to diverse natural images or require a lot of time for model train-ing, while simple models have limited representation capability. In this paper, we propose a simple, effective, robust, and fast (SERF) image super-resolver 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 datasets and experimental settings. The linear least square functions lead to closed-form solutions and therefore achieve computationally effi-cient implementations. To effectively decrease gaps, we group image patches into clusters using the k-means algorithm and learn a linear regress or for each cluster at each iteration. The cascaded learning process grad-ually decreases the gap of high-frequency 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 better efficiency than the existing state-of-the-art methods.