EN中文

Journal of Geo-information Science >

2012 , Vol. 14 >Issue 3: 352 - 357

DOI: https://doi.org/10.3724/SP.J.1047.2012.00352

ARTICLES

A Method to Quantitively Reduce Image Resolution Based on Multi-band Wavelet Transform

Expand
  • 1. Key Laboratory of Virtual Geographical Environment, Ministry of Education, Nanjing Normal University, Nanjing 210046, China;
    2. Department of Resource Management, Tangshan Teachers College; Tangshan 063000, China

Received date: 2011-11-14

  Revised date: 2012-05-04

  Online published: 2012-06-25

Fold

Abstract

The achievement of traditional image resolution reduction is primarily through image resampling. Multi-band wavelet is a newly developed branch of wavelet analysis, and its orthogonality, compact support and smoothness are better than the binary wavelet. In this paper we firstly described the demand of quantitative reduction of image resolution and the traditional methods on image resolution reduction. Then we used multi-band wavelet to reduce image resolution quantitatively based on multi-resolution time frequency characteristics. We did the image resolution reduction experiment through the 3-band wavelet transformation and extracted the low-frequency information of the 3-band wavelet as the resulting image that achieved the image resolution reduction. Lastly, compared with the results of common image re-sampling methods, such as the nearest method, bilinear interpolation method and derivative neighborhood averaging method qualitatively and quantitatively, we found that multi-band wavelet has obvious advantages in image resolution reduction.

Key words： image; quantitative evaluation; multi-band wavelet; resolution reduction

Cite this article

ZHANG Yanan, ZHU Changqing, DU Fuguang . A Method to Quantitively Reduce Image Resolution Based on Multi-band Wavelet Transform[J]. Journal of Geo-information Science, 2012 , 14(3) : 352 -357 . DOI: 10.3724/SP.J.1047.2012.00352

References

[1] 明东萍,王群,杨建宁.遥感影像空间尺度特征与最佳空间分辨率选择[J].遥感学报,2008,12(4):529-537.

[2] Chen J, Deng M, Xiao P F. Optimal spatial scale choosing for high resolution imagery based on texture frequency analysis[J]. Journal of Remote Sensing,2011,15(3):492-501.

[3] 柏延臣,王劲峰.遥感信息的不确定性研究分类与尺度效应模型[M].北京:地质出版社,2003.

[4] 李永忠.基于小波变换的多分辨率图像转换[J].江苏科技大学学报(自然科学版),2005,19(6):50-53.

[5] 杨晓梅,朱长青.多进制小波变换及其在影像分析中的应用[J].中国图象图形学报,1999,4(2):157-160.

[6] 马蔼乃.遥感概论[M].北京:科学出版社,1984,153-156.

[7] 季统凯,赵忠明.海啸地区遥感图像空间分辨率定量评价研究[J].遥感学报:2005,9(4):486-493.

[8] 朱长青.小波分析理论与影像分析[M].北京:测绘出版社,1998.

[9] 朱长青,史文中.空间分析建模与原理[M].北京:科学出版社,2006.

[10] 毕宁.多进制小波在图像处理中的应用 .中山大学论文,2004.

[11] 李小春,陈鲸.多进制小波变换的快速构造及在遥感图像融合中的实现[J].计算机工程与应用,2004(7):32-34,114.

[12] 闫利,岳昔娟,崔晨风.一种定量确定遥感融合图像空间分辨率的方法[J]. 武汉大学学报(信息科学版),2007,32(8):667-670.

Options
Outlines

/

〈 〉