Gradient entropy metric and p-Laplace diffusion constraint-based algorithm for noisy multispectral image fusion

	Gradient entropy metric and p-Laplace diffusion constraint-based algorithm for noisy multispectral image fusion
	Zhao, W. D.; Z. J. Xu and J. Zhao
	2016
发表期刊	Information Fusion
卷号	27
摘要	Noise is easily mistaken as useful features of input images, and therefore, significantly reducing image fusion quality. In this paper, we propose a novel gradient entropy metric and p-Laplace diffusion constraint-based method. Specifically, the method is based on the matrix of structure tensor to fuse the gradient information. To minimize the negative effects of noise on the selections of image features, the gradient entropy metric is proposed to construct the weight for each gradient of input images. Particularly, the local adaptive p-Laplace diffusion constraint is constructed to further suppress noise when rebuilding the fused image from the fused gradient field. Experimental results show that the proposed method effectively preserves edge detail features of multispectral images while suppressing noise, achieving an optimal visual effect and more comprehensive quantitative assessments compared to other existing methods. (C) 2015 Elsevier B.V. All rights reserved.
文章类型	期刊
收录类别	SCI ; EI
语种	英语
文献类型	期刊论文
条目标识符	http://ir.ciomp.ac.cn/handle/181722/57471
专题	中科院长春光机所知识产出
推荐引用方式 GB/T 7714	Zhao, W. D.,Z. J. Xu and J. Zhao. Gradient entropy metric and p-Laplace diffusion constraint-based algorithm for noisy multispectral image fusion[J]. Information Fusion,2016,27.
APA	Zhao, W. D.,&Z. J. Xu and J. Zhao.(2016).Gradient entropy metric and p-Laplace diffusion constraint-based algorithm for noisy multispectral image fusion.Information Fusion,27.
MLA	Zhao, W. D.,et al."Gradient entropy metric and p-Laplace diffusion constraint-based algorithm for noisy multispectral image fusion".Information Fusion 27(2016).

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