Earticle

초록

영어

A multi-spectral image fusion algorithm based on red-black wavelet (RBW) and principal component analysis (PCA) is proposed in this paper to enhance the image performance. The PCA algorithm was used to extract the diverse features and details of the multi-spectral image, and, then, these features were decomposed by RBW and fused by the improved diverse algorithms using low-frequency and high-frequency coefficients at different scales, frequency domains, decomposition layers, and frequency bands. Finally, these fused features and multi-spectral images were reconstructed by RBW and PCA inversion. The results of our experiments showed that the proposed algorithm provided higher spatial resolution and more excellent spectral information. In addition, it improved the processing speed, took less memory, and offers the potential for real-time processing.

목차

Abstract
 1. Introduction
 2. Red-Black Wavelet Transform
  2.1. Horizontal/Vertical Lifting
  2.2. Diagonal Lifting
 3. PCA- and RBW-based Multi-spectral Image Fusion
 4. Experimental Results and Analysis
  4.1. Parametric Analysis
  4.2. Experimental Results
  4.3. Objective Quantitative Analysis
 5. Conclusions
 References

키워드

저자

• Wenkao Yang [ Beijing Jiaotong University, Department of Electronic and Information Engineering, Beijing, China ]
• Jing He [ Beijing Jiaotong University, Department of Electronic and Information Engineering, Beijing, China ]
• Hongyuan Wang [ Dalian Polytechnic University, School of information science and engineering, Dalian, China ]
• Yue Feng [ Beijing Jiaotong University, Department of Electronic and Information Engineering, Beijing, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605