To increase the information of the fused image, this paper proposes an improved multi-modality image fusion algorithm that combines the complementary advantages of the non-subsampled shearlet transform (NSST) and discrete wavelet transform (DWT). NSST was used to decompose the two source images in multiscale and multi-direction to obtain the corresponding high-frequency and low-frequency sub-bands. The low-frequency sub-bands were further decomposed into low-frequency energy sub-bands and low-frequency detail sub-bands by the DWT, and the low-frequency energy sub-bands were fused by the maximum selection rules. An adaptive pulse-coupled neural network with improved connection strength (ICSAPCNN) was used to fuse the detailed sub-bands and high-frequency sub-bands, and the energy sub-bands and detailed sub-bands were fused by inverse DWT to obtain the fused low-frequency sub-bands. The NSST inverse transform was used to reconstruct the fusion image with rich details. The experimental results verified that the proposed algorithm is superior to the other algorithms in both subjective vision and objective evaluation and can be applied to the fusion of both infrared and visible source images and medical source images.