The Novel method of Face Hallucination

doi:null

您当前的位置：

首页 >

文章列表页 >

The Novel method of Face Hallucination

Article | 更新时间：2020-08-12

- The Novel method of Face Hallucination
- Optics and Precision Engineering Vol. 16, Issue 5, Pages: 815-821(2008)
- 作者机构：
  
  四川大学电子信息学院图像研究所
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Received：08 October 2007，
  
  Revised：15 January 2008，
  
  Published Online：22 May 2008，
  
  Published：22 May 2008
- 稿件说明：
移动端阅览
吴炜. 一种新颖的人脸图像超分辨率技术研究[J]. 光学精密工程, 2008,16(5):815-821

The Novel method of Face Hallucination[J]. Optics and precision engineering, 2008, 16(5): 815-821.
吴炜. 一种新颖的人脸图像超分辨率技术研究[J]. 光学精密工程, 2008,16(5):815-821 DOI：

The Novel method of Face Hallucination[J]. Optics and precision engineering, 2008, 16(5): 815-821. DOI：

摘要

本文针对基于学习的人脸图像超分辨率技术进行研究，采用多分辨率的幻觉脸算法。针对Baker方法（文献[1]）建立的图像金字塔提取高频细节不够丰富的缺点，采用Kirsch算子提取高频特征。它与Baker的一阶、二阶灰度算子结合，能够提取更多的图像信息，使得匹配更为准确。另外将流形学习中的LLE算法思想引入匹配复原过程中，使复原结果获取更完备的高频信息。实验结果表明，本文预测得到的先验模型更为准确，使得最终复原的人脸图像具有更好的视觉效果。

Abstract

This paper focuses on study of super-resolution image of face(Face Hallucination). A novel face hallucination algorithm is proposed in this paper based on multi-resolution pyramid structure. Different from Baker method (reference [1])

We add Kirsch operator to get high-frequency feature of image. Kirsch feature combined with the first and second order gray feature can extract more image information

make the matching process more accurate. In addition

we introduce one of the manifold learning methods

locally linear embedding (LLE) for matching process

to make priori model more accurate

more comprehensive

and having more higher frequency information. Experimental results show that this model predicted a priori be more accurate

it will make the recovery of facial images with better visual effect.

关键词

Keywords

references

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Classification of Hyperspectral remote sensing images using correlation neighbor LLE

Semi-supervised dual path network for hyperspectral image classification

Deep manifold reconstruction belief network for hyperspectral remote sensing image classification

Multi-features manifold discriminant embedding for hyperspectral image classification

Hyper-spectral image classification using spatial-spectral manifold reconstruction

Related Author

LIU Yi-zhe

HUANG Hong

LUO Fu-lin

LIU Jia-min

Zhengying LI

Ling JI

Zhen ZHANG

Hong HUANG

Related Institution

Key Laboratory of Optoelectronic Technique System of the Ministry of Education, Chongqing University

Key Laboratory of Optoelectronic Technology and System， Ministry of Education， Chongqing University

The 34th Research Institute of China Electronics Technology Group Corporation

Key Laboratory of Optoelectronic Technique System of the Ministry of Education, Chongqing University

Key Laboratory of Opto-electronic Technique of the Ministry of Education, Chongqing University

AI问答

Address：No.3888 Dong Nanhu Road, Changchun, Jilin, China Postal code：130033
Tel：0431-86176855 Email：gxjmgc@ciomp.ac.cn
Technical support is provided by Beijing Founder electronics co., LTD 吉ICP备11002662号-17 京公网安备11010802024621
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰