关键词: 实例学习, 流形学习, 实例回归, 字典学习, 单帧图像超分辨, 图像质量评价

Abstract: This paper comprehensively surveys the development of example learning-based single image Super-Resolution（SR） technique. The example learning-based single SR methods apply machine learning technology to estimate the high-frequency details missing in a given Low-Resolution（LR） images to produce a High-Resolution（HR） one with sharper edges and finer textures by learning the mapping relationship between the LR and HR images from a training set. According to how to use the examples and how to learn the relationships in the process of SR reconstruction, the existing example learning-based SR methods can be divided into five typical categories, i.e., k-Nearest Neighbors（k-NN）learning-based methods, manifolds learning-based methods, dictionary learning-based methods, example multi-linear regression-based methods, and deep learning-based methods. The main idea of each type and the representative SR approaches are introduced in detail. Then the SR results of six representative SR methods are compared and analyzed. Finally, the deep insight into example learning-based SR methods and promising future directions are summarized.

Key words: example learning, manifolds learning, example regression, dictionary learning, single image super-resolution, image quality assessment