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The aim of multi-focus image fusion is to combine multiple images with different focuses for enhancing the perception of a scene. The critical issue in the design of multi-focus image fusion algorithms is to evaluate the local content information of the input images. Motivated by the observation that the marginal distribution of the wavelet coefficients is different for images with different focus levels, an image fusion approach using wavelet-domain statistics is proposed in this paper. The proposed approach exploits the spreading of the wavelet coefficients distribution to measure the degree of the image's blur. Furthermore, the wavelet coefficients distribution is evaluated using a locally-adaptive Laplacian mixture model. Extensive experiments are conducted using three sets of test images under three objective metrics to demonstrate the superior performance of the proposed approach.