基于SVM和窗口梯度的多焦距图像融合方法

Multi-focus image fusion based on support vector machines and window gradient

为提高多焦距图像融合质量,提出了一种基于支持向量机(SVM)和窗口梯度的多焦距图像融合方法。该方法首先对多焦距图像进行基于窗口的经验模态分解(WEMD),得到一组内涵模式函数分量(高频)和残余分量(低频),WEMD可以有效解决图像分解中的信号混叠问题;然后,利用SVM的输出指导低频分量融合,选取更清晰的聚焦区域;利用本文的窗口梯度对比算法指导高频分量融合,在保持融合图像对比度的同时保证图像的一致性;最后,经过WEMD逆变换得到融合图像。在9组多焦距图像上进行实验,从主观评价和5种客观评价指标方面,本文的融合方法相比于其他5种方法能获得更好的融合质量。

In order to improve the quality of multi-focus image fusion,a multi-focus image fusion method based on support vector machines(SVM)and window gradient is proposed in this paper.First,the multifocus images are decomposed by window empirical mode decomposition(WEMD),and a set of intrinsic mode function components(high frequency part)and residual components(low frequency part)are obtained.WEMD can effectively solve the signal aliasing problem in image decomposition.Then,the fusion rule of low-frequency components is determined by the output of the support vector machine,and the clearer focus area is selected.The window gradient contrast algorithm proposed in this paper is used to guide the fusion of high-frequency components,and the consistency of the image is ensured while maintaining the contrast of the fused image.Finally,the WEMD inverse transform is performed to obtain the fused image.Experiments were carried out on 9 sets of multi-focus images.Results show that the proposed method can obtain better fusion quality than the other five methods in terms of the subjective evaluation and five objective evaluation indicators.