Earticle

초록

영어

Image super resolution reconstruction has important significance in remote sensing image feature extraction and classification etc.. Because the remote sensing image size is larger, it is difficult to super resolution reconstruction using multiple images, the compressed sensing (CS) theory was introduced into the super-resolution reconstruction. Algorithm designed the low pass filter to reduce the sample correlation matrix and wavelet, at the same time, the algorithm selects the partial Hadamard-matrix as the measurement matrix, it has faster reconstruction speed and low storage requirements, which ensure that the image reconstruction keep with the RIP criterion of compressed sensing theory . Finally, this paper realizes the remote sensing image super resolution reconstruction through the improved iterative algorithm. Experiments show that the reconstructed images of the PSNR value has increased, the reconstructed image has a better visual effect.

목차

Abstract
 1. Introduction
 2. The Compressed Sensing Theory
 3. The Super Resolution Reconstruction Algorithm based on Compressed Sensing
  3.1 The Construction of Learning Dictionary
  3.2 The Algorithm of Super-resolution Resco0nstruction Based on CS
 4. The Experimental Analysis
 5. Summary
 Acknowledgements
 References

키워드

저자

• Qiang Yang [ College of Geophysical, ChengDu University of Technology, China, College of Computer and Information Engineering, Yibin University, China ]
• HuaJun 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 Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605