Name
Title | ||
|---|---|---|
A block-based orthogonal locality preserving projection method for face super-resolution |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/978-3-642-28490-8_27 | ACIIDS |
Keywords | Field | DocType |
face image,block-based orthogonal locality,super-resolution process,face manifold,general regression neural network,projection method,surveillance system,complete face,orthogonal locality preserving projections,face super-resolution,promising result,local structure,super resolution,manifold | Forehead,Locality,General regression neural network,Pattern recognition,Computer science,Local structure,Projection method,Artificial intelligence,Superresolution,Manifold,Machine learning | Conference |
Volume | ISSN | Citations |
7197 | 0302-9743 | 3 |
PageRank | References | Authors |
0.38 | 9 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shwu-huey Yen | 1 | 42 | 9.07 |
| Che-Ming Wu | 2 | 3 | 0.38 |
| Hung-Zhi Wang | 3 | 3 | 0.38 |