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A classification-based image fusion scheme using wavelet transform

机译:基于小波变换的基于分类的图像融合方案

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摘要

Image fusion is used to combine multiple images of the same scene into a comprehensive representation. However, an image fusion method does not suit all fusion requirements in practice. In this paper, we introduce an image fusion framework based on wavelet transform, which is designed to satisfy the fusion applications as much as possible. The input images are firstly decomposed into wavelet domain. While analyzing the different frequency coefficients, the principles of fusion are discussed and adaptively assigned according to the properties of subimages. Finally, the fused image can be reconstructed via wavelet inverse transform. The experimental results show that our framework can preserve most features of original images, and the algorithm has some resistance to noise
机译:图像融合用于将同一场景的多个图像组合成一个完整的表示形式。但是,图像融合方法实际上并不适合所有融合要求。在本文中,我们介绍了一种基于小波变换的图像融合框架,旨在尽可能满足融合应用的需求。首先将输入图像分解为小波域。在分析不同的频率系数时,讨论了融合原理,并根据子图像的属性进行自适应分配。最后,可以通过小波逆变换来重建融合图像。实验结果表明,我们的框架能够保留原始图像的大部分特征,并且该算法具有一定的抗噪能力。

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