Dictionary Learning for Image Super-resolution

摘要：

　　Recently,single image super-resolution reconstruction via sparse representation has attracted increasing interest.In this paper,we propose a new method for image super-resolution using a local sparse model on image patches.We introduce a new dictionary training formulation,which enforces that the sparse representation of a low-resolution image patch can well reconstruct its underlying high-resolution image patch,and we adopt an effective stochastic gradient algorithm to solve the corresponding optimization problem.Considering the scale of the recovered high-resolution image patch has been altered in sparse recovery,we introduce an efficient method to find its correct scale.Moreover,the high-resolution deficiency image is reconstructed by the proposed super-resolution method and compensated to better preserve the high-frequency details of images.Compared with the recently proposed joint dictionary learning method for image super-resolution,the experimental results of our method show visual,PSNR and SSIM improvements.

关键词： Super-resolution dictionary learning sparse representation deficiency compesation

作者: LI Juan WU Jin YANG Shen LIU Jin

作者单位: College of Information Science and Technology,Wuhan University of Science and Technology,Wuhan 430081,China;Engineering Research Center of Metallurgical Automation and Measurement Technology,Wuhan 430081,China

会议类型: 国际会议

会议名称: The 33th Chinese Control Conference第33届中国控制会议

会议地点: 南京

会议语种:英文

页码: 7195-7199

在线出版日期: 2014-07-28（万方平台首次上网日期，不代表论文的发表时间）