核偏最小二乘算法的图像超分辨率算法

吴炜; 杨晓敏; 余艳梅; 石一兴; 何小海

doi:10.3969/j.issn.1001-0548.2011.01.020

核偏最小二乘算法的图像超分辨率算法

四川大学电子信息学院成都 610064

基金项目:

教育部重点项目(107094);国家自然科学基金(61071161)

详细信息

作者简介:
吴炜(1975-)，男，博士后，主要从事图像处理、图像识别和图像通信方面的研究.

中图分类号: TP391.1
计量
- 文章访问数: 4735
- HTML全文浏览量: 145
- PDF下载量: 114
出版历程
- 收稿日期: 2009-08-07
- 修回日期: 2010-05-18
- 刊出日期: 2011-02-14

Image Super-Resolution Using KPLS

College of Electronics and Information Engineering,Sichuan University Chengdu 610064

摘要

摘要: 提出了基于核偏最小二乘算法(KPLS)回归的超分辨率复原算法。该算法首先将高低分辨率图像块的高频信息和中频信息作为建立回归关系的特征，并对图像进行分块；依据相应的高低分辨率图像块的关系，使用KPLS建立起回归模型；在复原时，依据该模型回归得到高分辨率的图像块，将图像块拼接为高分辨率的图像。通过对人脸图像和车牌图像的实验结果，表明该算法无论是对人脸图像还是车牌图像都能取得较好的复原效果。
- 图像复原 /
- 核偏最小二乘法(KPLS) /
- 基于学习的超分辨率 /
- 回归算法
Abstract: A learning-based super-resolution algorithm based on Kernel Partial Least Squares (KPLS) regression is proposed. First, KPLS regression algorithm is introduced. Then a super-resolution algorithm based on KPLS regression is analyzed. High resolution images use the high-frequency information as their feature, while low resolution images use middle-frequency as their features. Based on the relationship of the high and low resolution images, KPLS is used to set up regression model. The regression model is applied to infer high-resolution image. The experimental results show that our method can achieve very good results to face images and car plate images. The results of our method are closer to the real images.
- image restoration /
- Kernel partial least squares (KPLS) /
- learning-based super-resolution /
- regression algorithm