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首页> 外文期刊>Journal of Imaging Science and Technology >Multi-modal Image Fusion Algorithm based on Variable Parameter Fractional Difference Enhancement
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Multi-modal Image Fusion Algorithm based on Variable Parameter Fractional Difference Enhancement

机译：基于变量参数分数差增强的多模态图像融合算法

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摘要

AbstractMulti-modal image fusion can more accurately describe the features of a scene than a single image. Because of the different imaging mechanisms, the difference between multi-modal images is great, which leads to poor contrast of the fused images. Therefore, a simpleand effective spatial domain fusion algorithm based on variable parameter fractional difference enhancement is proposed. Based on the characteristics of fractional difference enhancement, a variable parameter fractional difference is introduced, the multi-modal images are repeatedly enhanced,and multiple enhanced images are obtained. A correlation coefficient is applied to constrain the number of enhancement cycles. In addition, an energy contrast is used to extract the contrast features of the image, and the tangent function is simultaneously used to obtain the fusion weightto attain multiple contrast-enhanced initialization fusion images. Finally, the weighted average is applied to obtain the final fused image. Experimental results demonstrate that the proposed fusion algorithm can effectively preserve the contrast features between images and improve the qualityof fused images.
机译：摘要可以更准确地描述比单个图像更准确地描述场景的功能。由于不同的成像机制，多模态图像之间的差异很大，这导致融合图像的对比度差。因此，提出了一种基于变量参数分数差差增强的SimpleAnd有效的空间域融合算法。基于分数差增强的特征，引入了可变参数分数差，重复增强多模态图像，获得多种增强图像。应用相关系数来限制增强循环的数量。另外，使用能量对比度来提取图像的对比度，并且切线函数同时用于获得融合权重达到多个对比度增强型初始化融合图像。最后，应用加权平均值以获得最终熔融图像。实验结果表明，所提出的融合算法可以有效地保持图像之间的对比度，提高融合图像的质量。

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