Computer Science ›› 2018, Vol. 45 ›› Issue (12): 210-216.doi: 10.11896/j.issn.1002-137X.2018.12.035

• Graphics, Image & Pattern Recognition • Previous Articles     Next Articles

Improved Image Reconstruction Algorithm Based on L1-Norm and TV Regularization

XU Min-da1, LI Zhi-hua1,2   

  1. (School of Internet of Things Engineering,Jiangnan University,Wuxi,Jiangsu 214122,China)1
    (Engineering Research Center of IoT Technology Application,Ministry of Education,Wuxi,Jiangsu 214122,China)2
  • Received:2017-12-18 Online:2018-12-15 Published:2019-02-25

Abstract: Concerning the streak artifacts and noise in the image reconstruction for incomplete projection data,this paper presented a image reconstruction model integrating L1 and TV regularization.Based on this model,this paper proposed a new image reconstruction method combining Bregman iteration and TV soft-thresholding filter.In the proposed method,the projection data are first applied to carry out preliminary reconstruction through Bregman iteration,and then the iterative results are used to minimize the TV model.At last,by repeating the above two steps,the reconstructed ima-ge can be obtained.To demonstrate its effectiveness,the Shepp-Logan model without noise and the Abdomen model with noise were employed to take experiments.The proposed algorithm not only has better visual effects,but also has more excellent performance compared with the existing algorithms such as ART,LSQR,L1 and BTV etc.Experimental results show that the proposed algorithm can well preserve image details and edges,and possesses good anti-noise capability,while eliminating streak artifacts effectively.

Key words: Bregman iteration, Image iteration reconstruction, L1-norm regularization, Total variation soft-thresholding

CLC Number: 

  • TP317.4
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