期刊論文
| 學年 | 100 |
|---|---|
| 學期 | 1 |
| 出版（發表）日期 | 2012-01-01 |
| 作品名稱 | A Block-Based Orthogonal Locality Preserving Projection Method for Face Super-Resolution |
| 作品名稱（其他語言） | |
| 著者 | Yen, Shwu-huey; Wu, Che-ming; Wang, Hung-zhi |
| 單位 | 淡江大學資訊工程學系 |
| 出版者 | Heidelberg: Springer Berlin Heidelberg |
| 著錄名稱、卷期、頁數 | Lecture Notes in Computer Science 7197, pp.253-262 |
| 摘要 | Due to cost consideration, the quality of images captured from surveillance systems usually is poor. To restore the super-resolution of face images, this paper proposes to use Orthogonal Locality Preserving Projections (OLPP) to preserve the local structure of the face manifold and General Regression Neural Network (GRNN) to bridge the low-resolution and high-resolution faces. In the system, a face is divided into four blocks (forehead, eyes, nose, and mouth). The super-resolution process is applied on each block then combines them into a complete face. Comparing to existing methods, the proposed method has shown an improved and promising result. |
| 關鍵字 | Orthogonal Locality Preserving Projections; OLPP; manifold; super-resolution; General Regression Neural Network; GRNN |
| 語言 | en_US |
| ISSN | 0302-9743 |
| 期刊性質 | 國外 |
| 收錄於 | EI |
| 產學合作 | |
| 通訊作者 | |
| 審稿制度 | 是 |
| 國別 | DEU |
| 公開徵稿 | |
| 出版型式 | 電子版 |
| 相關連結 |
機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/78692 ) |