An AK-BRP dictionary learning algorithm for video frame sparse representation in compressed sensing

Article

Abstract

Sparsifying transform is an important prerequisite in compressed sensing. And it is practically significant to research the fast and efficient signal sparse representation methods. In this paper, we propose an adaptive K-BRP (AK-BRP) dictionary learning algorithm. The bilateral random projection (BRP), a method of low rank approximation, is used to update the dictionary atoms. Furthermore, in the sparse coding stage, an adaptive sparsity constraint is utilized to obtain sparse representation coefficient and helps to improve the efficiency of the dictionary update stage further. Finally, for video frame sparse representation, our adaptive dictionary learning algorithm achieves better performance than K-SVD dictionary learning algorithm in terms of computation cost. And our method produces smaller reconstruction error as well.

Keywords

Bilateral random projections (BRP) Adaptive K-BRP algorithm Dictionary learning Sparse representation K-SVD algorithm 

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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.School of ScienceNanjing University of Posts and TelecommunicationsNanjingChina
  2. 2.Center for Visual Cognitive Computation and Its ApplicationNanjing University of Posts and TelecommunicationsNanjingChina

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