燕山大学学报Issue(3)：233-237,242,6.DOI:10.3969/j.issn.1007-791X.2014.03.008
基于双层混合字典学习的图像超分辨率复原
Image super-resolution recovery based on dual-mixed dictionary learning
摘要
Abstract
A new method of dictionary learning is proposed for image super-resolution, which is named dual-mixed dictionary. Among them, the first layer dictionary uses semi-coupled dictionary, which ensures the flexibility and accuracy of the recovery process, and combines with sparse representation algorithm to get the first layer of the restored image. In order not to affect the overall computing speed, the second layer dictionary adopts classification dictionary, and uses the difference between the original image and the first layer of the restored image as the high resolution sample to restore more high frequency details. The experiment results show that the proposed algorithm has remarkable improvement in visual quality and peak signal-to-noise ratio in comparison with the traditional image super-resolution algorithm based on single dictionary, effectively improves the quality of the images.关键词
字典学习/超分辨率/稀疏表示/双层混合Key words
dictionary learning/super-resolution/sparse representation/dual-mixed分类
信息技术与安全科学引用本文复制引用
李志全,马莹,郭士亮,王志斌..基于双层混合字典学习的图像超分辨率复原[J].燕山大学学报,2014,(3):233-237,242,6.基金项目
国家自然科学基金资助项目(61107039) （61107039）
