A fusion method for infrared and visible images was developed based on target enhancement and butterfly optimization to address the problems of target blurring， textural detail loss， and artifacts in traditional infrared and visible image fusion results. First， to deal with the blurring of the fusion target edge caused by the imaging mechanism of the infrared image， an infrared image enhancement module based on target edge enhancement was constructed. Second， a visible light enhancement module based on multiscale Retinex with color restoration was developed to solve the problem of missing details in fused images caused by the low quality of visible images. Third， fourth-order partial differential equations and the principal component analysis method were used to smooth out the edges of infrared and visible enhanced images to solve the problem of artifacts in the fusion results. Finally， an image reconstruction module based on butterfly optimization was designed for the adaptive allocation of reconstructed image weights. This allows the target to be highlighted in the final fusion result while retaining more textural details. To verify the advantages of the proposed algorithm， it was compared with six classic algorithms on the TNO， INO， M3FD， and RoadScene datasets. The experimental results show that the fusion results obtained by the proposed algorithm have clear edges， strong contrast， no artifacts， and rich textural details. Compared with the other algorithms， the objective evaluation indicators EN， SF， SD， JE， VIF， and NSS are improved by an average of 9.24%， 38.88%， 51.11%， 4.65%， 35.44% and 19.36%， respectively.