Adaptive multi-focus image fusion using a wavelet-based statistical sharpness measure

Multi-focus image fusion aims to combine a set of images that are captured from the same scene but with different focuses for producing another sharper image. 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, a new statistical sharpness measure is proposed in this paper by exploiting 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. The proposed sharpness measure is then exploited to perform adaptive image fusion in wavelet domain. Extensive experiments are conducted using three sets of test images under three objective metrics to demonstrate the superior performance of the proposed approach.

► A new wavelet-domain statistical sharpness measure is proposed.
► The spreading of wavelet coefficients indicates sharpness of local image.
► Use locally adaptive Laplacian mixture model for wavelet coefficient distribution.