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.