Multiple-view face hallucination by a novel regression analysis in tensor space
Author
Source
The International Arab Journal of Information Technology
Issue
Vol. 13, Issue 6 (31 Dec. 2016)8 p.
Publisher
Publication Date
2016-12-31
Country of Publication
Jordan
No. of Pages
8
Main Subjects
Information Technology and Computer Science
Topics
Abstract EN
In this paper, the n^^el multiple-view face hallucination method was proposed.
This method is reconstructed the high-resolution face images in various poses (normal, up, down, left, and right) from a single low-resolution face image within these poses.
There are two steps in our proposed method.
In the first step, a high-resolution face image in the same view of the observation is reconstructed by the position-patch face hallucination framework with the improved Locally Linear Embedding (LLE), which the number of neighbours is adaptive.
In the second step, the reconstructed image is used to generate the high-resolution of the other views by the novel tensor regression technique.
The experimental results on the well-known dataset show that the proposed method can achieve the better quality image than the baseline methods
American Psychological Association (APA)
Sanguansat, Parinya. 2016. Multiple-view face hallucination by a novel regression analysis in tensor space. The International Arab Journal of Information Technology،Vol. 13, no. 6.
https://search.emarefa.net/detail/BIM-654845
Modern Language Association (MLA)
Sanguansat, Parinya. Multiple-view face hallucination by a novel regression analysis in tensor space. The International Arab Journal of Information Technology Vol. 13, no. 6 (Dec. 2016).
https://search.emarefa.net/detail/BIM-654845
American Medical Association (AMA)
Sanguansat, Parinya. Multiple-view face hallucination by a novel regression analysis in tensor space. The International Arab Journal of Information Technology. 2016. Vol. 13, no. 6.
https://search.emarefa.net/detail/BIM-654845
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-654845