期刊論文

標題 A Block-Based Orthogonal Locality Preserving Projection Method for Face Super-Resolution
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 英文(美國)
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
國別 德國
公開徵稿
出版型式 電子版