基于嵌入式多尺度分解和可能性理论的多波段纹理图像融合

将多尺度变换和“高频取大、低频加权平均”融合规则相结合是融合双波段图像的有效方法。但用该类方法融合多波段图像时，序贯式加权常常会导致原图像间固有的差异信息在融合图像中被弱化，从而影响后续的目标识别和场景理解。该问题在融合具有纹理特征的多波段图像时更为突出。为此，提出了一个基于嵌入式多尺度分解和可能性理论的多波段纹理图像融合新方法。首先，利用一种多尺度变换方法把多波段原图像分别分解为高频和低频成分，并对多波段图像中标准差最大的一幅原图像的低频成分利用另一种多尺度方法进行分块，再以该分块图像的大小和位置为标准对其余波段的原图像进行分块。然后，基于可能性理论的相关融合规则逐一融合对应的多波段块图像，再把块融合图像进行拼接，以拼接结果作为低频融合图像。最后，将该低频融合图像和利用取大规则融合得到的高频成分一起通过多尺度逆变换获得最终的融合图像。这种方法不仅将像素级和特征级融合方法综合在一起, 而且将空间域和变换域技术综合在一起, 并通过对大小块采用不同融合规则解决了目标边缘的锯齿效应问题。实验表明该方法效果显著。

Multi-Band Texture Image Fusion Based on the Embedded Multi-Scale Decomposition and Possibility Theory

The combination of multi‐scale transform and the rules which are “high‐frequency coefficients com‐bined by selecting the maximum gray value or energy”and“low‐pass ones combined by weighting average” is an effective method in dual‐band image fusion .However ,when these methods are used to fuse multi‐band ima‐ges ,sequential weighted average often leads the weakening of the inherent different information of original im‐ages ,which affects the subsequent target recognition and scene understanding .The problem is more obvious when fusing multi‐band images with texture features .In order to describe the scene in a more comprehensive and precise way ,a new multi‐band texture image fusion method based on embedded multi‐scale decomposition and possibility theory is proposed .The method consists of three parts .The original multi‐band images are de‐composed into their high‐and low‐frequency components through a multi‐scale transform .The high‐frequency components are fused per‐pixel by extracting the maximum gray value ,whereas the last layer of low‐frequency components of original multi‐band images with the largest standard deviation is blocked through the another multi‐scale transform .Based on the specific sizes and positions of these blocks ,the remaining two original ima‐ges are divided .All the blocks from three bands are traversely fused according to the possibility theory ,and the low‐frequency image is formed by mosaicing these fused blocks .Then ,this image is inversely transformed with its high‐frequency counterparts to get the final fusion image .This method not only integrates the pixel‐level with feature‐level fusion methods ,but also integrates the space domain with transform domain technolo‐gies together ,and solves the problem of saw tooth effect on the edge of the target through the different fusion rules with the different sizes of blocks .The validity of the method proposed is proved .