Paper Info
Title
CT Image Reconstruction by Spatial-Radon Domain Data-Driven Tight Frame Regularization
Abstract
This paper proposes a spatial-Radon domain computed tomography (CT) image reconstruction model based on data-driven tight frames (SRD-DDTF). The proposed SRD-DDTF model combines the idea of the joint image and Radon domain inpainting model of Dong, Li, and Shen [J. Sci. Compd., 54 (2013), pp. 333-349] and that of the data-driven tight frames for image denoising [J.-F. Cai, H. Ji, Z. Shen, and G.-B. Ye, Appl. Compd. Harmon. Anal., 37 (2014), p. 89-105]. It is different from existing models in that both the CT image and its corresponding high quality projection image are reconstructed simultaneously using sparsity priors by tight frames that are adaptively learned from the data to provide optimal sparse approximations. An alternative minimization algorithm is designed to solve the proposed model, which is nonsmooth and nonconvex. Convergence analysis of the algorithm is provided. Numerical experiments show that the SRD-DDTF model is superior to the model of Dong, Li, and Shen [J. Sci. Compd., 54 (2013), pp. 333-349] especially in recovering some subtle structures in the images.
Year
DOI
Venue
2016
10.1137/16M105928X
SIAM JOURNAL ON IMAGING SCIENCES
Keywords
Field
DocType
computed tomography,data-driven tight frames,sparse approximation,spatial-Radon domain re-construction
Convergence (routing),Iterative reconstruction,Computer vision,Data-driven,Radon,Inpainting,Regularization (mathematics),Artificial intelligence,Prior probability,Tight frame,Mathematics
Journal
Volume
Issue
ISSN
9
3
1936-4954
Citations 
PageRank 
References 
1
0.35
30
Authors
2
Name
Order
Citations
PageRank
ruohan zhan111.36
Bin Dong226130.04