Low-dose X-ray computed tomography image reconstruction using edge sparsity regularization

Shousheng Luo, Keke Kang, Yang Wang, Xue-Cheng TAI

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

Abstract

Total variation (TV) regularization is one of popular techniques for low dose x-ray computed tomography image reconstruction. However, the reconstruction image by TV method often suffers staircase effect. In this paper, we propose an edge sparsity model, which penalizes the difference between L1 norm and L2 norm of gradient, for low dose x-ray computed tomography image reconstruction. Alternating direction method of multipliers (ADMM) is adopted to solve the proposed model. Experiment results on simulation data and real data are presented to verify the effectiveness of the proposed method.

Original languageEnglish
Title of host publicationISICDM 2019 - Conference Proceedings
Subtitle of host publication3rd International Symposium on Image Computing and Digital Medicine
PublisherAssociation for Computing Machinery
Pages303-307
Number of pages5
ISBN (Electronic)9781450372626
DOIs
Publication statusPublished - 24 Aug 2019
Event3rd International Symposium on Image Computing and Digital Medicine, ISICDM 2019 - Xi'an, China
Duration: 24 Aug 201926 Aug 2019

Publication series

NameACM International Conference Proceeding Series

Conference

Conference3rd International Symposium on Image Computing and Digital Medicine, ISICDM 2019
Country/TerritoryChina
CityXi'an
Period24/08/1926/08/19

Scopus Subject Areas

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

User-Defined Keywords

  • ADMM
  • Edge sparsity regularization
  • Low dose
  • XCT image reconstruction

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