计算机技术与发展2017，Vol.27Issue(5)：35-39,45,6.DOI:10.3969/j.issn.1673-629X.2017.05.008
基于双稀疏字典的新型盲压缩感知模型
A Novel Blind Compressed Sensing Model Based on DoubleSparsity Dictionary
摘要
Abstract
Aiming at the problem of simultaneous image recovery and dictionary learning from Compressed Sensing (CS) measurements,where the sparse basis or the dictionary is unknown prior in the practical application of CS theory,in view of the existing Blind Compressed Sensing (BCS) theory,a novel Blind Compressed Sensing (D-BCS) algorithm based on the double sparsity dictionary has been proposed.It is an iterative method that alternates between sparse-coding and dictionary update steps.In the sparse coding stage of the proposed scheme,a split Bregman iteration based technique has been utilized to solve the non-convex l1 minimization problem to update the sparse coefficient matrix.And in the dictionary update stage,the optimization function is converted into a LASSO-like problem and the dictionary atoms are updated column by column using LASSO algorithm.Comparative simulation experimental results of several test video frames with a variety of sampling ratios have demonstrated that the proposed algorithm can recover the original signal from CS measurements very well and exhibits state-of-the-art performance improvements in its ability for recovery of the compressed video frames.关键词
盲压缩感知/双稀疏字典/分裂Bregman迭代/LASSO/稀疏表示Key words
blind compressed sensing/double sparsity dictionary/split Bregman iteration/LASSO/sparse representation分类
信息技术与安全科学引用本文复制引用
钱阳,李雷,石曼曼..基于双稀疏字典的新型盲压缩感知模型[J].计算机技术与发展,2017,27(5):35-39,45,6.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）
