基于前向-后向自校正扩散引导特征重建的图像融合

An image fusion method using forward-backward self-correcting diffusion guided feature reconstruction

现有图像融合方法不同程度地存在边缘阶梯效应,导致一些空间伪影引入融合图像.鉴于此,提出一种新的解决图像融合过程中鲁棒性差的方法—–前向-后向自校正扩散引导特征重建(forward-backward self-correcting diffusion,FBSD),对分解后各特征之间的差异设计一种基于期望值最大算法和主成分分析的混杂融合策略.最后利用评价指标评估所提出算法的性能,验证了所提出方法在边缘阶梯效应的处理上优于现有的图像融合方法,同时验证了融合决策的有效性.

The existing image fusion methods have an edge ladder effect in varying degrees,which leads to some spatial artifacts introduced into the fused image.This paper proposes a new method to solve the poor robustness in the process of image fusion,that is,forward-backward self-correcting diffusion(FBSD) guided feature reconstruction.According to the differences between features after decomposition,a hybrid fusion strategy based on expectation-maximization algorithm and principal component analysis algorithm is designed.Finally,the evaluation index is used to evaluate the performance of the proposed algorithm,and it is verified that this method is better than the existing image fusion methods in dealing with the edge step effect,and the fusion decision is effective.