New Robust Principal Component Analysis for Joint Image Alignment and Recovery via Affine Transformations, Frobenius and L2,1 Norms
Author
Source
International Journal of Mathematics and Mathematical Sciences
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-04-10
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
This paper proposes an effective and robust method for image alignment and recovery on a set of linearly correlated data via Frobenius and L2,1 norms.
The most popular and successful approach is to model the robust PCA problem as a low-rank matrix recovery problem in the presence of sparse corruption.
The existing algorithms still lack in dealing with the potential impact of outliers and heavy sparse noises for image alignment and recovery.
Thus, the new algorithm tackles the potential impact of outliers and heavy sparse noises via using novel ideas of affine transformations and Frobenius and L2,1 norms.
To attain this, affine transformations and Frobenius and L2,1 norms are incorporated in the decomposition process.
As such, the new algorithm is more resilient to errors, outliers, and occlusions.
To solve the convex optimization involved, an alternating iterative process is also considered to alleviate the complexity.
Conducted simulations on the recovery of face images and handwritten digits demonstrate the effectiveness of the new approach compared with the main state-of-the-art works.
American Psychological Association (APA)
Likassa, Habte Tadesse. 2020. New Robust Principal Component Analysis for Joint Image Alignment and Recovery via Affine Transformations, Frobenius and L2,1 Norms. International Journal of Mathematics and Mathematical Sciences،Vol. 2020, no. 2020, pp.1-9.
https://search.emarefa.net/detail/BIM-1172700
Modern Language Association (MLA)
Likassa, Habte Tadesse. New Robust Principal Component Analysis for Joint Image Alignment and Recovery via Affine Transformations, Frobenius and L2,1 Norms. International Journal of Mathematics and Mathematical Sciences No. 2020 (2020), pp.1-9.
https://search.emarefa.net/detail/BIM-1172700
American Medical Association (AMA)
Likassa, Habte Tadesse. New Robust Principal Component Analysis for Joint Image Alignment and Recovery via Affine Transformations, Frobenius and L2,1 Norms. International Journal of Mathematics and Mathematical Sciences. 2020. Vol. 2020, no. 2020, pp.1-9.
https://search.emarefa.net/detail/BIM-1172700
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1172700