Linear Total Variation Approximate Regularized Nuclear Norm Optimization for Matrix Completion
Joint Authors
Shu, Huazhong
Han, Xu
Wu, Jiasong
Wang, Lu
Chen, Yang
Senhadji, L.
Source
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-05-28
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
Matrix completion that estimates missing values in visual data is an important topic in computer vision.
Most of the recent studies focused on the low rank matrix approximation via the nuclear norm.
However, the visual data, such as images, is rich in texture which may not be well approximated by low rank constraint.
In this paper, we propose a novel matrix completion method, which combines the nuclear norm with the local geometric regularizer to solve the problem of matrix completion for redundant texture images.
And in this paper we mainly consider one of the most commonly graph regularized parameters: the total variation norm which is a widely used measure for enforcing intensity continuity and recovering a piecewise smooth image.
The experimental results show that the encouraging results can be obtained by the proposed method on real texture images compared to the state-of-the-art methods.
American Psychological Association (APA)
Han, Xu& Wu, Jiasong& Wang, Lu& Chen, Yang& Senhadji, L.& Shu, Huazhong. 2014. Linear Total Variation Approximate Regularized Nuclear Norm Optimization for Matrix Completion. Abstract and Applied Analysis،Vol. 2014, no. 2014, pp.1-8.
https://search.emarefa.net/detail/BIM-1014748
Modern Language Association (MLA)
Han, Xu…[et al.]. Linear Total Variation Approximate Regularized Nuclear Norm Optimization for Matrix Completion. Abstract and Applied Analysis No. 2014 (2014), pp.1-8.
https://search.emarefa.net/detail/BIM-1014748
American Medical Association (AMA)
Han, Xu& Wu, Jiasong& Wang, Lu& Chen, Yang& Senhadji, L.& Shu, Huazhong. Linear Total Variation Approximate Regularized Nuclear Norm Optimization for Matrix Completion. Abstract and Applied Analysis. 2014. Vol. 2014, no. 2014, pp.1-8.
https://search.emarefa.net/detail/BIM-1014748
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1014748