To reserve or enhance the integrity of regional heterogeneous features, a feature transfer image fusion method based on Fuzzy lifting (FTF) is proposed, which includes a fuzzy lifting stage, a feature transfter process and a new reference composite feature evaluation index. Different from traditional fusion methods which usually adopts an undifferentiated representation and a global unified fusion method for the original image, as for the infrared and visible image fusion, imaging characteristics and visual interest features at different physical locations are different, so the important local features on each images may be lost or weaken during the fusion process. The above problems can be solved by proposed FTF method from two aspects. Firstly, in the fuzzy lifting stage, the Fourth order Partial Differential Equation (FPDE) and fuzzy region rules are crossly performed to remove redundant information and highlight the main features of the region. Secondly, a feature transfer model is established to perform a more efficient feature transfer on the lifted feature map. Besides, considering the shortcomings of existing indexes in feature description, a new ComPosite Feature metric (CPF) for fused images are also proposed. Qualitative and quantitative comparisons are made between the FTF method and other eight state-of-art methods on TNO image fusion dataset. The experimental results show that the salient thermal radiation targets and clear visible details can be represented by the final fused images, which is conducive to the monitoring and analysis. In summary, the proposed FTF method is more effective, and has better ability to catch most of the salient feature from source images than that of state-of-art methods.

为了保留或增强区域异构特征的完整性，提出了一种基于模糊提升（FTF）的特征转移图像融合方法，该方法包括模糊提升阶段，特征转移过程和新的参考复合特征评价指标。与通常对原始图像采用不区分表示和全局统一融合方法的传统融合方法不同，就红外和可见图像融合而言，不同物理位置的成像特征和视觉兴趣特征是不同的，因此重要的局部特征在融合过程中，每个图像可能会丢失或减弱。通过提出的FTF方法可以从两个方面解决上述问题。首先，在模糊提升阶段，交叉执行四阶偏微分方程（FPDE）和模糊区域规则，以删除冗余信息并突出显示区域的主要特征。其次，建立特征转移模型以对提升的特征图执行更有效的特征转移。此外，考虑到现有指标在特征描述中的缺点，提出了一种新的融合图像ComPosite特征度量（CPF）。在TNO图像融合数据集上，FTF方法与其他八种最新方法之间进行了定性和定量比较。实验结果表明，最终的融合图像可以代表显着的热辐射目标和清晰可见的细节，有利于监测和分析。总而言之，提出的FTF方法更有效，