The convergence of the block cyclic projection with an overrelaxation parameter for compressed sensing based tomography

The convergence of the block cyclic projection for compressed sensing based tomography (BCPCS) algorithm had been proven recently in the case of underrelaxation parameter λ∈(0,1]. In this paper, we prove its convergence with overrelaxation parameter λ∈(1,2). As a result, the convergence of the other...

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Veröffentlicht in:Journal of computational and applied mathematics Jg. 280; S. 59 - 67
Hauptverfasser: Arroyo, Fangjun, Arroyo, Edward, Li, Xiezhang, Zhu, Jiehua
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 15.05.2015
Schlagworte:
Algorithms
Amalgamated projection method
Block iterative algorithm
Blocking
Compressed
Compressed sensing
Convergence
Detection
Image reconstruction
Mathematical analysis
Projection
Tomography
Total variation minimization
Amalgamated projection method
Total variation minimization
Block iterative algorithm
Compressed sensing
Image reconstruction
ISSN:0377-0427, 1879-1778
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Zusammenfassung:The convergence of the block cyclic projection for compressed sensing based tomography (BCPCS) algorithm had been proven recently in the case of underrelaxation parameter λ∈(0,1]. In this paper, we prove its convergence with overrelaxation parameter λ∈(1,2). As a result, the convergence of the other two algorithms (BCAVCS and BDROPCS) with overrelaxation parameter λ∈(1,2) in a special case is derived. Experiments are given to demonstrate the convergence behavior of the BCPCS algorithm with different values of λ.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:0377-0427
1879-1778
DOI:10.1016/j.cam.2014.11.036