Infrared and Visible Image Fusion via Sparse Representation and Adaptive Dual-Channel PCNN Model Based on Co-Occurrence Analysis Shearlet Transform

The principle of image fusion is to integrate complementary information of the heterogeneous images to obtain a fused image that is more in line with the visual effect of the human eyes. However, most decomposition methods cannot distinguish the textures and edges in an image, which is easy to produce the halo artifacts around edges. In this article, we proposed a novel image decomposition strategy co-occurrence analysis shearlet transform (CAST) to preprocess the input images depending on the co-occurrence statistic information to generate the base- and detail-layer components. In order to improve the sparseness of the base layer, the classified sparse dictionary in the measurement domain is introduced to enhance the robustness of incorrect registration. As for the detail layers, the adaptive dual-channel pulse-coupled neural network (PCNN) model is adopted as the fusion rule, in which the neurons are activated by the improved spatial frequency (ISF) operator, and the model uses the sum of improved weighted Laplacian energy (SIWLE) as the adaptive linking strength. Finally, the fused image can be generated by the inverse CAST. Based on the combination of the sparseness of the classified dictionary and the visual characteristics of PCNN model, the more valuable information of the source images can be fused so that the final fused images conform to the human visual system. Qualitative and quantitative experimental results demonstrate the superiority of the proposed method over other typical fusion techniques on publicly available datasets.