Semiblind Image Deconvolution with Spatially Adaptive Total Variation Regularization
Joint Authors
Ruan, Yaduan
Chen, Qimei
Fang, Houzhang
Source
Mathematical Problems in Engineering
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-08-27
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
A semiblind image deconvolution algorithm with spatially adaptive total variation (SATV) regularization is introduced.
The spatial information in different image regions is incorporated into regularization by using the edge indicator called difference eigenvalue to distinguish flat areas from edges.
Meanwhile, the split Bregman method is used to optimize the proposed SATV model.
The proposed algorithm integrates the spatial constraint and parametric blur-kernel and thus effectively reduces the noise in flat regions and preserves the edge information.
Comparative results on simulated images and real passive millimeter-wave (PMMW) images are reported.
American Psychological Association (APA)
Ruan, Yaduan& Fang, Houzhang& Chen, Qimei. 2014. Semiblind Image Deconvolution with Spatially Adaptive Total Variation Regularization. Mathematical Problems in Engineering،Vol. 2014, no. 2014, pp.1-8.
https://search.emarefa.net/detail/BIM-1044389
Modern Language Association (MLA)
Ruan, Yaduan…[et al.]. Semiblind Image Deconvolution with Spatially Adaptive Total Variation Regularization. Mathematical Problems in Engineering No. 2014 (2014), pp.1-8.
https://search.emarefa.net/detail/BIM-1044389
American Medical Association (AMA)
Ruan, Yaduan& Fang, Houzhang& Chen, Qimei. Semiblind Image Deconvolution with Spatially Adaptive Total Variation Regularization. Mathematical Problems in Engineering. 2014. Vol. 2014, no. 2014, pp.1-8.
https://search.emarefa.net/detail/BIM-1044389
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1044389
