Fusion of infrared and visible images through multi-level co-occurrence filtering

Infrared and visible image fusion aims to obtain an integrated image which contains more recognizable information. To attain this object, an effective infrared and visible image fusion algorithm through multi-level co-occurrence filtering is proposed in this paper. Firstly, the input images are decomposed into three layers through a co-occurrence filtering decomposition model. Secondly, a gradient-domain-based pulse-coupled neural network (PCNN) fusion strategy is applied in the three layers. Finally, the fused image is reconstructed by the three fused layers. Experiments show that the proposed algorithm outperform most state-of-the-art algorithms in both qualitative and quantitative measures.