Earticle

초록

영어

The traditional image fusion algorithm completed the fusion based on all pixel information. The time and space requirements are higher. The improved fusion algorithm used the theory of compressed sensing (CS) for the processing of remote sensing image fusion. Firstly, the source images using wavelet transform for sparse representation, then, the improved fusion algorithm used the observation matrix for image dimension measurement, and completed the image fusion in CS domain. Finally, the algorithm used the improved OMP algorithm to reconstruct the fused image. The improved fusion algorithm is only applied with a few measurement data of the compressed sensing, and overcomed the shortcomings of traditional pixel level fusion, the fusion algorithm achieved good experimental data.

목차

Abstract
 1. Introduction
 2. The Compressed Sensing Theory
  2.1 The Sparse Representation of Signals
  2.2 Reduced Dimension Measurement
  2.3 Signal Reconstruction
 3. Analysis of Several Problems in Application of Compressed Sensing
  3.1 Analysis of Signal Sparse Representation Problem
  3.2 Analysis of Measurement Matrix Problems
  3.3 Analysis of Signal Reconstruction Problem
 4. Remote Sensing Image Fusion Based on Compressed Sensing
 5. The Experimental Analysis
 6. Summary
 Acknowledgements
 References

키워드

저자

• Qiang Yang [ College of Geophysical, ChengDu University of Technology, China, College of Computer and Information Engineering, Yibin University, China ]
• Hua Jun Wang [ College of Geophysical, ChengDu University of Technology, China ]
• Xuegang Luo [ College of Geophysical, ChengDu University of Technology, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Hybrid Information Technology

• 간기

  격월간

• pISSN

  1738-9968

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605