A Sparsity Basis Selection Method for Compressed Sensing

作者：Bi, Dongjie^*; Xie, Yongle; Li, Xifeng; Rosa, Yahong

来源：IEEE Signal Processing Letters, 2015, 22(10): 1738-1742.

DOI：10.1109/LSP.2015.2429748

• Basis selection
• compressed sensing (CS)
• sparsity maximization
• sparsity

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 l(1) 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 Peyre in 2010.

• 出版日期2015-10
• 单位电子科技大学