An Image Fusion Method with Sparse Representation Based on Genetic Algorithm Optimization

doi:10.13190/j.jbupt.2016.02.015

JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM ›› 2016, Vol. 39 ›› Issue (2): 73-76,87.doi: 10.13190/j.jbupt.2016.02.015

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An Image Fusion Method with Sparse Representation Based on Genetic Algorithm Optimization

ZHAO Xue-jun, LI Yu-zhen, LEI Shu-yu

School of Mechanical Electronic and Information Engineering, China University of Mining and Technology, Beijing 100083, China

Received:2015-05-29 Online:2016-04-28 Published:2016-04-28

Abstract

Abstract:

Due to sparse nature of the nature of image, the sparse signal representation theory can be well used in image processing, and with sparse representation theory of continuous improvement, it is also widely used in image de-noising rehabilitation and integration process. The sparse representation of image fusion theory was used to determine the weighting factor fusion rules sparse coefficients, and to solve the optimal weighting coefficients of genetic algorithm to achieve image fusion panchromatic, multispectral images, contourlet transform, principal component analysis (PCA) algorithm and the high-pass filter image fusion algorithm. Also it improves the image clarity spectral fidelity compared to other algorithms.

Key words: genetic algorithms, sparse representation, image fusion

CLC Number:

TP751.1

ZHAO Xue-jun, LI Yu-zhen, LEI Shu-yu. An Image Fusion Method with Sparse Representation Based on Genetic Algorithm Optimization[J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2016, 39(2): 73-76,87.

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An Image Fusion Method with Sparse Representation Based on Genetic Algorithm Optimization

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