针对目前传统多聚焦图像融合中图像局部模糊不易度量, 融合策略难以设计等问题,提出一种新的相位拉伸核函数, 形成基于扩展相位拉伸变换的多聚焦图像融合算法。该算法将传统的线性或次线性群延迟相位滤波器推广到非线性群延迟相位滤波器,并从理论上证明,这种扩展相位拉伸变换的逆变换相位近似于原始图像的归一化二阶梯度, 将图像高频特征传统的梯度极值表达转换为角度或相位表达,利用角度/相位图像局部方差对清晰与模糊图像良好的区别特性设计出基于扩展相位拉伸变换局部相位方差度量的融合策略,克服了目前融合方法存在的不足。利用MATLAB软件平台对Lytro数据集中的相当数量多聚焦图像数据进行融合实验, 与传统基于离散小波变换、拉普拉斯、超分辨率、引导滤波和联合卷积自编码网络算法等融合算法结果进行对照分析。 结果表明, 本文算法的融合图像明显优于传统最好的融合算法, 融合图像的互信息、信息熵、空间频率、平均梯度及结构相似性等指标比现有的其他方法提高5%以上,证明了所提算法的优越性与实用性。

Aiming at the problem that the local blur of the image is not easy to measure and the fusion strategy is difficult to designed in traditional multi-focus image fusion, a new phase stretching kernel function is developed, which results in a multi-focus image fusion algorithm based on extended phase stretch transformation. The method promotes the traditional linear or sublinear group delay phase filter to the nonlinear group delay phase filter. It is proved theoretically that the phase of the inverse transformation of the extended phase stretch transformation is approximate to the normalized two-step degree of the original image. The traditional gradient extremum expression of image high-frequency features is transformed into angle or phase expression, and a fusion strategy based on local phase variance measurement of extended phase stretching transform is designed to overcome the shortcomings of current fusion methods by using the good difference between clear and fuzzy images. Many multi focus image data in Lytro dataset are fused using MATLAB software platform. The results are compared with those of traditional fusion algorithms based on discrete wavelet transformation, Laplace Laplacian, super-resolution, guided filtering, and joint convolution self-coding network algorithm. The results show that the fusion image of this algorithm is obviously better than the traditional best fusion algorithm, and the mutual information, information entropy, spatial frequency, average gradient and structural similarity of the fused image are improved by more than 5% compared with other existing methods, which proves the superiority and practicability of the proposed algorithm.