원문정보
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
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