计算机应用研究2016，Vol.33Issue(3)：911-915,5.DOI:10.3969/j.issn.1001-3695.2016.03.062
基于双正则化参数的在线字典学习超分辨率重建
Image super-resolution based on online dictionary learning with two regularization parameters
摘要
Abstract
The performance of some learning-based super-resolution methods are promising,but some obvious artifacts appear in the reconstruction images.In order to solve this problem,this paper presented a novel super-resolution algorithm based on online dictionary learning (ODL)with two regularization parameters.It employed ODL in the dictionary learning procedure. Then the algorithm set two regularization parameters in the procedures of dictionary learning and image reconstruction.In the experiments,the PSNRs of the new method were 0.39 dB higher than the state-of-the-art sparse coding super-resolution (SCSR)in average.It could eliminate the artifacts while recovering the edge sharpness and the texture details efficiently.With the introduction of ODL and two regularization parameters,it promoted the dictionary training accuracy and made the sparse coefficients in dictionary learning and image reconstruction adjustable separately.The experiments show that the artifacts are eliminated effectively.It promotes the final effect of super-resolution reconstruction well.关键词
正则化参数/超分辨率/在线字典学习/稀疏编码/图像Key words
regularization parameters/super-resolution(SR)/online dictionary learning/sparse coding/image分类
信息技术与安全科学引用本文复制引用
倪浩,阮若林,刘芳华..基于双正则化参数的在线字典学习超分辨率重建[J].计算机应用研究,2016,33(3):911-915,5.基金项目
国家自然科学基金资助项目（61271256）；湖北省高等学校优秀中青年科技创新团队计划项目（T201513）；湖北省自然科学基金资助项目 （）
