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Color multi-focus image fusion algorithm based on fuzzy theory and dual-tree complex wavelet transform

机译:基于模糊理论和双树复小波变换的彩色多焦点图像融合算法

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This paper puts forward a new color multi-focus image fusion algorithm based on fuzzy theory and dual-tree complex wavelet transform for the purpose of removing uncertainty when choosing sub-band coefficients in the smooth regions. Luminance component is the weighted average of the three color channels in the IHS color space and it is not sensitive to noise. According to the characteristics, luminance component was chosen as the measurement to calculate the focus degree. After separating the luminance component and spectrum component, Fisher classification and fuzzy theory were chosen as the fusion rules to conduct the choice of the coefficients after the dual-tree complex wavelet transform. So fusion color image could keep the natural color information as much as possible. This method could solve the problem of color distortion in the traditional algorithms. According to the simulation results, the proposed algorithm obtained better visual effects and objective quantitative indicators.
机译:提出了一种基于模糊理论和双树复小波变换的彩色多焦点图像融合算法,以消除在平滑区域选择子带系数时的不确定性。亮度分量是IHS色彩空间中三个色彩通道的加权平均值,并且对噪声不敏感。根据特性,选择亮度分量作为测量值以计算聚焦度。在分离出亮度分量和光谱分量后,选择Fisher分类和模糊理论作为融合规则,进行双树复小波变换后的系数选择。因此,融合彩色图像可以尽可能保留自然色信息。该方法可以解决传统算法中色彩失真的问题。仿真结果表明,该算法取得了较好的视觉效果和客观的量化指标。

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