Compressive large-scale image sensing

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Cost-efficient compressive sensing of large-scale images with fast reconstructed high-quality results is very challenging. In this paper, we propose a new compressive large-scale image sensing method, composed of operator-based strategy in the context of fixed point continuation technique and weighted LASSO with tree structure sparsity pattern. The main characteristic of our method is free from any assumptions and restrictions. The feasibility of our method is verified via computational complexity and convergence analyses, extensive simulations, and comparisons with state-of-the-art algorithms.

Original languageEnglish
Title of host publication2015 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages378-382
Number of pages5
ISBN (Electronic)9781479975914
DOIs
Publication statusPublished - 2016 Feb 23
EventIEEE Global Conference on Signal and Information Processing, GlobalSIP 2015 - Orlando, United States
Duration: 2015 Dec 132015 Dec 16

Publication series

Name2015 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2015

Other

OtherIEEE Global Conference on Signal and Information Processing, GlobalSIP 2015
CountryUnited States
CityOrlando
Period15-12-1315-12-16

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Signal Processing

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