An efficient algorithm for overcomplete sparsifying transform learning with signal denoising
type: Item
instance of: scholarly article
title: An efficient algorithm for overcomplete sparsifying transform learning with signal denoising
zbMATH Open document ID: 1400.94051
DOI: 10.1155/2016/6329740
author: Beiping Hou
author: Zhihui Zhu
author: Gang Li
author: Aihua Yu
published in: Mathematical Problems in Engineering
publication date: 2018-10-12T00:00:00Z
review text: Summary: 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.
Mathematics Subject Classification ID: 94A12
zbMATH DE Number: 6953429
Wikidata QID: Q59140867
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2016/6329740
OpenAlex ID: W2506997306
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description: scientific article; zbMATH DE number 6953429