摘要： 基于学习的超分辨率算法使用一个图像训练集来产生一个学习模型，运用该模型为输入的低分辨率图像创建更多的高频信息，获得比基于重建算法更好的结果。该文介绍了基于学习的超分辨率技术的相关工作、理论基础和主要算法，提出基于学习的超分辨率算法中仍需解决的关键问题，展望其在未来的研究发展方向。

关键词: 超分辨率, 马尔可夫随机场, 图像金字塔

Abstract: Learning-based super-resolution technique predicts the high-resolution images from the input low-resolution ones, through learning from a training set which consists of a large number of other high-resolution images. And the results are better than the reconstruction based super-resolution algorithms. The related work, theory and algorithms of learning-based super-resolution are illustrated. The crucial problems which need to be resolved in further work are proposed. Directions of future research are pointed.

Key words: super-resolution, Markov random field, image pyramid

中图分类号: 

• TP391.41