Infrared and visible image fusion based on WEMD and generative adversarial network reconstruction

To overcome the problem of blurred edges and low contrast in the fusion of infrared and visible images, a two-dimensional window empirical mode decomposition (WEMD) and infrared and visible light image fusion algorithm for GAN reconstruction was proposed. The infrared and visible light images were decomposed using WEMD to obtain the intrinsic mode function components (IMF) and residual components. The IMF components were fused through principal component analysis, and the residual components were fused by the weighted average. The preliminary fused image was reconstructed and input into the GAN to play against the visible light image, and some background information was supplemented to obtain the final fusion image. The average gradient (AG), edge strength (EI), entropy (EN), structural similarity (SSIM), and mutual information (MI) are used for objective evaluation, and they increased by 46.13%, 39.40%, 19.91%, 3.72%, and 33.10%, respectively, compared with the other five methods. The experimental results show that the proposed algorithm achieves better retention of the edge and texture details of the sources image while simultaneously highlighting the target of the infrared image, has better visibility, and has obvious advantages in terms of objective evaluation indicators. © 2022, Science Press. All right reserved.