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Image fusion based on image decomposition using self-fractional Fourier functions - Springer

机译:使用自分数阶傅里叶函数基于图像分解的图像融合-Springer

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摘要

Image fusion has been receiving increasing attention in the research community in a wide spectrum of applications. Several algorithms in spatial and frequency domains have been developed for this purpose. In this paper we propose a novel algorithm which involves the use of fractional Fourier domains which are intermediate between spatial and frequency domains. The proposed image fusion scheme is based on decomposition of source images (or its transformed version) into self-fractional Fourier functions. The decomposed images are then fused by maximum absolute value selection rule. The selected images are combined and inverse transformation is taken to obtain the final fused image. The proposed decomposition scheme and the use of some transformation before the decomposition step offer additional degrees of freedom in the image fusion scheme. Simulation results of the proposed scheme for different transformation of the source images for two different sets of images are also presented. It is observed through the simulation results that the use of taking the transformation before the decomposition step improves the quality of fused image. In particular the results of using the fractional Fourier transform and discrete cosine transform before the decomposition step are encouraging.
机译:图像融合已在广泛的应用领域中引起了研究界的越来越多的关注。为此目的,已经开发了几种空间和频域算法。在本文中,我们提出了一种新颖的算法,其中涉及使用介于空间域和频域之间的分数阶傅立叶域。所提出的图像融合方案基于将源图像(或其变换后的版本)分解为自分数傅立叶函数。然后,通过最大绝对值选择规则对分解后的图像进行融合。组合所选图像并进行逆变换以获得最终的融合图像。所提出的分解方案以及在分解步骤之前使用某些变换为图像融合方案提供了额外的自由度。还给出了针对两组不同图像的源图像的不同变换所提出的方案的仿真结果。通过仿真结果可以看出,在分解步骤之前进行变换可以提高融合图像的质量。特别地,在分解步骤之前使用分数阶傅里叶变换和离散余弦变换的结果令人鼓舞。

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