基于四叉树分解和自适应焦点测度的多聚焦图像融合

Multi-focus Image Fusion Based on Quadtree Decomposition and Adaptive Focus Measure

为了克服基于块的融合方法对块的大小敏感以及融合图像中存在伪影等问题,提出一种新的基于四叉树分解和自适应焦点测度的多聚焦图像融合方法。首先,设计一种新的基于修正拉普拉斯能量和(SML)和导向滤波的自适应焦点测度,用于获得源图像的焦点图。然后,采用一种新的四叉树分解策略,并结合已经得到的焦点图,进一步将源图像分解成最优大小的树块;同时,从树块中检测出聚焦区域,并构成决策图。最后,对决策图进行优化和一致性验证,并重构出一幅全聚焦图像。通过公共多聚焦图像数据集进行实验,与11种先进的融合方法进行视觉质量和客观指标比较。实验结果表明,本文所提出的融合方法取得了更好的性能。

In order to overcome the problem that the block-based fusion method is sensitive to the size of the block and the artifacts in the fused image,a new multi-focus image fusion method based on quadtree decomposition and adaptive focus measurement is proposed.Firstly,according to a new adaptive focus measure based on the sum of modified Laplacian(SML)and guided filtering,it is used to obtain the focus map of the source image.Then,using a new quadtree decomposition strategy and combining the obtained focus map,the source image is further decomposed into tree blocks of optimal size,the focus area is detected from the tree blocks,and a decision map is formed.Finally,the consistency of the decision graph is optimized and verified,and a fully focused image is reconstructed.Through experiments on public multi-focus image data sets,visual quality and objective indicators are compared with 9 advanced fusion methods.The experimental results show that the fusion method proposed in this paper has achieved better performance.