Tree structure sparsity pattern guided convex optimization for compressive sensing of large-scale images

Wei-Jie Liang, Gang Xuan Lin, Chun Shien Lu

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

Cost-efficient compressive sensing of large-scale images with quickly reconstructed high-quality results is very challenging. In this paper, we present an algorithm to solve convex optimization via the tree structure sparsity pattern, which can be run in the operator to reduce computation cost and maintain good quality, especially for large-scale images. We also provide convergence analysis and convergence rate analysis for the proposed method. The feasibility of our method is verified through simulations and comparison with the state-of-the-art algorithms.

Original languageEnglish
Article number7762896
Pages (from-to)847-859
Number of pages13
JournalIEEE Transactions on Image Processing
Volume26
Issue number2
DOIs
Publication statusPublished - 2017 Feb 1

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Convex optimization
Mathematical operators
Costs

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Graphics and Computer-Aided Design

Cite this

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Tree structure sparsity pattern guided convex optimization for compressive sensing of large-scale images. / Liang, Wei-Jie; Lin, Gang Xuan; Lu, Chun Shien.

In: IEEE Transactions on Image Processing, Vol. 26, No. 2, 7762896, 01.02.2017, p. 847-859.

Research output: Contribution to journalArticle

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