智能系统学报2017，Vol.12Issue(1)：8-14,7.DOI:10.11992/tis.201603039
基于稀疏表示与线性回归的图像快速超分辨率重建
Rapid super-resolution image reconstruction based on sparse representation and linear regression
摘要
Abstract
Single-image super-resolution aims at reconstructing a high-resolution image from a single low-resolution image.Recent methods relying on both neighborhood embedding and sparse coding have led to significant quality improvements.However, the application of these approaches is still practically difficult because they are either too slow or demand tedious parameter tweaks.In most of these methods, the speed and quality of image reconstruction are the two aspects that cannot be balanced easily.With regard to the abovementioned problems, this research proposed a rapid image super-resolution reconstruction algorithm based on linear regression, which effectively combined the sparse representation with the regression method.First, a dictionary was trained using the K-SVD algorithm based on training samples.Subsequently, the entire dataset was divided into a number of subspaces according to the atoms in the dictionary.Moreover, the mapping from low-to-high-resolution images can be independently obtained for each subspace.Finally, the high-resolution image was reconstructed by selecting the corresponding projection matrix.Experimental results demonstrate that both the image reconstruction quality and the speed of the proposed algorithm performed better than other widely used methods.关键词
线性回归/超分辨率/字典训练/稀疏表示/图像重建/特征训练/子空间/邻域嵌入Key words
linear regression/super-resolution/dictionary learning/sparse representation/image reconstruction/feature learning/subspace/neighborhood embedding分类
信息技术与安全科学引用本文复制引用
赵志辉,赵瑞珍,岑翼刚,张凤珍..基于稀疏表示与线性回归的图像快速超分辨率重建[J].智能系统学报,2017,12(1):8-14,7.基金项目
国家自然科学基金项目(61272028,61572067) （61272028,61572067）
国家"863"计划项目(2014AA015202) （2014AA015202）
广东省自然科学基金项目(2016A030313708) （2016A030313708）
北京市自然科学基金项目(4162050). （4162050）
