计算机技术与发展2017，Vol.27Issue(6)：36-40,5.DOI:10.3969/j.issn.1673-629X.2017.06.008
基于自适应K-SVD字典的视频帧稀疏重建算法
An Adaptive K-SVD Dictionary Learning Algorithm for Video Frame Sparse Reconstruction
摘要
Abstract
Finding sparsifying transforms is an important prerequisite of compressed sensing,which directly affects the reconstruction accuracy.It has practical significance to research the fast and efficient signal sparse representation method.Based on the traditional K-SVD algorithm,an adaptive K-SVD dictionary learning algorithm has been proposed to improve the speed and performance of dictionary training which is an iterative one that alternates between sparse coding and dictionary update steps.In the sparse coding stage,an adaptive sparsity constraint has been utilized to obtain sparser representation coefficient,which has further improved the efficiency of the dictionary update stage.And in the dictionary update stage,the dictionary atoms are updated column by column using the classic K-SVD dictionary update method.With the novel adaptive dictionaries as sparse representation for video frame compressed sensing,comparative experimental results demonstrate that the proposed adaptive K-SVD dictionary learning algorithm achieves better performance than traditional K-SVD algorithm in terms of running time.In addition,the new method has better signal sparse representation performance,and also can reduce the reconstruction error of compressed sensing.关键词
K-SVD算法/自适应K-SVD算法/字典学习/稀疏表示/压缩感知Key words
K-SVD algorithm/adaptive K-SVD algorithm/dictionary learning/sparse representation/compressed sensing分类
信息技术与安全科学引用本文复制引用
钱阳,李雷,袁安安..基于自适应K-SVD字典的视频帧稀疏重建算法[J].计算机技术与发展,2017,27(6):36-40,5.基金项目
国家自然科学基金资助项目(61070234,61071167,61373137,61501251) （61070234,61071167,61373137,61501251）
江苏省2015年度普通高校研究生科研创新计划项目(KYZZ15_0235) （KYZZ15_0235）
南京邮电大学引进人才科研启动基金资助项目(NY214191) （NY214191）
