Image fusion with Internal Generative Mechanism

• The Internal Generative Mechanism is brought into image fusion.
• A refined saliency detection method is proposed.
• Experiments on various images tested the effectiveness of the algorithm.

In this paper, an Internal Generative Mechanism (IGM) based fusion algorithm is proposed. In the algorithm, source images are decomposed into a coarse layer and a detail layer by simulating the mechanism of human visual system perceiving images; then, the algorithm fuses the detail layer using Pulse Coupled Neural Network (PCNN), and fuses the coarse layer by using the spectral residual based saliency method; finally, coefficients in all the fused layers are combined to obtain the final fused image. The interests of the algorithm lie in the fact that it accords with the basic principles of human visual system perceiving images and it can preserve detail information that exists in source images. Experiments on various images are conducted to test the effectiveness of the algorithm. The experimental results have shown that the final images fused by the proposed algorithm achieve satisfying visual perception; meanwhile, the algorithm is superior to other traditional algorithms in terms of objective measures.