an efficient algorithm for overcomplete sparsifying transform learning with signal denoising

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ID: 161274
2016
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Abstract
This paper deals with the problem of overcomplete transform learning. An alternating minimization based procedure is proposed for solving the formulated sparsifying transform learning problem. A closed-form solution is derived for the minimization involved in transform update stage. Compared with existing ones, our proposed algorithm significantly reduces the computation complexity. Experiments and simulations are carried out with synthetic data and real images to demonstrate the superiority of the proposed approach in terms of the averaged representation and denoising errors, the percentage of successful and meaningful recovery of the analysis dictionary, and, more significantly, the computation efficiency.
Reference Key
hou2016mathematicalan Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Beiping Hou;Zhihui Zhu;Gang Li;Aihua Yu
Journal journal of power sources
Year 2016
DOI
10.1155/2016/6329740
URL
http://dx.doi.org/10.1155/2016/6329740
Keywords
civil engineering (general)mathematics engineering (general)

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