Multi-focus image fusion based on pulse coupled neural network and WSEML in DTCWT domain

The goal of multi-focus image fusion is to merge near-focus and far-focus images of the same scene to obtain an all-focus image that accurately and comprehensively represents the focus information of the entire scene. The current multi-focus fusion algorithms lead to issues such as the loss of details and edges, as well as local blurring in the resulting images. To solve these problems, a novel multi-focus image fusion method based on pulse coupled neural network (PCNN) and weighted sum of eight-neighborhood-based modified Laplacian (WSEML) in dual-tree complex wavelet transform (DTCWT) domain is proposed in this paper. The source images are decomposed by DTCWT into low- and high-frequency components, respectively; then the average gradient (AG) motivate PCNN-based fusion rule is used to process the low-frequency components, and the WSEML-based fusion rule is used to process the high-frequency components; we conducted simulation experiments on the public Lytro dataset, demonstrating the superiority of the algorithm we proposed.