%0 Journal Article
%T 基于极限学习机的图像超分辨率算法
%A 刘翠响
%A 张凤林
%A 李敏
%J 河北工业大学学报
%D 2017
%R 10.14081/j.cnki.hgdxb.2017.02.003
%X 为了进一步提高基于学习的超分辨率图像重建质量，考虑到极限学习机（ELM） 具有学习速度快和良好数据预测与分析能力，提出了1 种基于极限学习机的图像超分辨率重建方法. 在图像稀疏思想下，将高分辨率图像中的高频细节信息作为原子构建冗余字典. 具体是提取训练图像的高频信息，采用改进的K-SVD 算法对高低分辨率图像进行字典学习，构建对应的特征字典作为极限学习机的输入训练网络参数，建立超分辨率重建模型. 最后仿真实验结果表明，所提算法能取得比对比算法更好的实验数据.</br>In order to further improve the quality of the learning-based super-resolution image reconstruction, considering the extreme learning machine (ELM) with fast learning speed and good data prediction and analysis, this paper proposes image super resolution reconstruction based on the extreme learning machine. Under the idea of image sparse, high-frequency details is used as atomic to construct redundant dictionary. Specifically, high frequency information of the training image is extracted. The improved K-SVD algorithm is used to carry out dictionary learning on high and low resolution images. The corresponding feature dictionary is constructed as the input to train network parameter. Super-resolution reconstruction model is established. Finally the simulation results show that the proposed algorithm can obtain better experimental data than the comparative algorithm.
%K 极限学习机
%K 字典学习
%K 超分辨率
%K 高频信息&lt
%K /br&gt
%K （School of Electronic and Information Engineering
%K Hebei University of Technology
%K Tianjin 300401
%K China）
%U http://zrxuebao.hebut.edu.cn//oa/darticle.aspx?type=view&id=201702003