Title: Remote sensing image fusion based on shearlet and genetic algorithm

Authors: Qiguang Miao; Ruyi Liu; Yining Quan; Jianfeng Song

Addresses: School of Computer Science and Technology, Xidian University, Xian, Shaanxi Province 710071, China ' School of Computer Science and Technology, Xidian University, Xian, Shaanxi Province 710071, China ' School of Computer Science and Technology, Xidian University, Xian, Shaanxi Province 710071, China ' School of Computer Science and Technology, Xidian University, Xian, Shaanxi Province 710071, China

Abstract: Image fusion is to combine information from two or more images of a scene into a single composite image, which will produce more information for visual perception or computer processing. In recent years, many algorithms have been developed, but there exist information loss and image distortion. To produce a satisfactory fusion result, an image fusion algorithm based on shearlet and genetic algorithm is proposed. As one of the multi-scale geometric analysis (MGA) tools, shearlet is equipped with a rich mathematical structure which is associated to a multi-resolution analysis. Genetic algorithm (GA) is an optimisation algorithm. So GA can be used to image fusion where parameter optimisation is required. Firstly, shearlet is performed on each input image to obtain their low-pass and high-pass coefficients. And then, GA is used to optimise the weighted factors in the fusion rule. Finally, the fused image is obtained by inverse shearlet transform. Experimental results have demonstrated that our method could acquire better fusion quality than any other methods.

Keywords: image fusion; shearlet; genetic algorithm; fitness function; multi-scale geometric analysis; MGA; fusion rule.

DOI: 10.1504/IJBIC.2017.084330

International Journal of Bio-Inspired Computation, 2017 Vol.9 No.4, pp.240 - 250

Received: 07 Dec 2016
Accepted: 09 Mar 2017

Published online: 05 Jun 2017 *

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