A Sparsity Basis Selection Method for Compressed Sensing
A Sparsity Basis Selection Method for Compressed Sensing
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DOI：
10.1109/lsp.2015.2429748
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发表时间：
2015-05
影响因子：
3.9
通讯作者：
Dongjie Bi;Yongle Xie;Xifeng Li;Y. R. Zheng
中科院分区：
文献类型：
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作者：
Dongjie Bi;Yongle Xie;Xifeng Li;Y. R. Zheng
这封信提出了一种新的稀疏基选择压缩感知方法（SBSCS），用于改善压缩感知（CS）测量的信号重建。基于不同类别的变换导致不同的稀疏性表达式和更好的稀疏性表达式导致更好的信号恢复的观察，所提出的SBSCS方法通过在CS最小化内嵌套稀疏性最大化来在一组冗余树结构字典中搜索最佳类别的变换和基。SBSCS方法自适应地选择类的变换和基的最佳稀疏性措施，在每一次迭代，并迅速收敛到最终类的变换和基。数值实验表明，所提出的SBSCS方法提高了信号恢复的质量比现有的最佳基压缩感知方法（BBCS）Peyre '在2010年提出的。
This letter presents a new sparsity basis selection compressed sensing method (SBSCS) for improving signal reconstruction from compressed sensing (CS) measurements. Based on the observation that different classes of transform cause different sparsity expressions and better sparsity expression leads to better signal recovery, the proposed SBSCS method searches the best class of transform and basis in a set of redundant tree-structured dictionaries by nesting sparsity maximization within the CS minimization. The SBSCS method adaptively selects the class of transform and basis with the best sparsity measure at each ℓ1 iteration and converges quickly to the final class of transform and basis. Numerical experiments show that the proposed SBSCS method improves the quality of signal recovery over the existing best basis compressed sensing method (BBCS) proposed by Peyré in 2010.