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中文摘要：
针对序贯式融合方法常常会降低多波段图像间原有差异的问题，提出了基于嵌入式多尺度变换（embedded multi‐scale transform ，EM T ）和局部差异特征的多波段图像融合方法．利用支持度变换法（support value transform ，SVT）分别分解多波段图像；再采用四叉树（quad‐tree ，QT）法分解灰度值较分散的某波段图像的最后一层低频成分图像，以分解得到的块图像为标准分别分割其他波段的最后一层低频成分图像；采用可能性理论的析取融合规则对多波段低频块图像进行特征级融合；遍历所有块得到低频融合图像块；将拼接得到的低频融合图像与像素级逐层融合得到的支持度图像序列进行逆变换，获得最终融合图像．实验结果表明：四叉树分解融合有显著效果，与单纯的四叉树融合相比，嵌入式多尺度分解融合图像的边缘强度提高了13．31％，对比度提升了2．63％，熵提高了4．26 ％，运行时间下降了87．11％，证明了所提出方法的有效性．
英文摘要：
Multi‐band images fusion can improve the effect of the target detection .In view of the differences among multi‐band images often reduced by using the sequential fusion ,a method of multi‐band image fusion is proposed by embedded multi‐scale transform （EM T ） and local difference feature . The detailed procedure is shown as follows ：Firstly ,multi‐band images are decomposed respectively with support value transform （SVT） .Secondly ,using the method of quad‐tree （QT） ,the last layer of low‐frequency image for most dispersed grey value image is decomposed into blocks which are regarded as the standard to decompose the others＆#39;last layer of low‐frequency image .Thirdly ,using disjunctive combination of the possibility theory ,corresponding blocks of the multi‐band images are fused in feature‐level .Then ,all blocks are traversed to get low frequency fused block images which are mosaicked . Lastly , the final image is got through inverse transformation of mosaic image and support sequence fused image .The fused results of visible image ,infrared medium‐wave image and long‐wave image show that ：the effect is significant based on quad‐tree decomposition;compared with the simple quad‐tree decomposition fusion , the method of EM T successfully increases the edge intensity by 13 .31% ,the contrast ratio by 2 .63% ,the entropy by 4 .26% and decreases the running time by 87 .11% .T hus the validity of the method is proved .
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