Infrared and Visible Image Fusion Based on Multi-scale and Attention Model

Aiming at the problems that infrared and visible images are prone to artifacts and unclear outlines of small targets after fusion, an infrared and visible images fusion algorithm based on the combination of multi-scale features and attention model is proposed. The feature maps of different scales of the source image are extracted through five times of down-sampling, and then the infrared and visible image feature maps of the same scale are input to the fusion layer based on the attention model to obtain an enhanced fusion feature map. Finally, the small-scale fusion feature map is up-sampled five times, and then added to the feature map of the same scale after up-sampling, until the scale is consistent with the source image, and the multi-scale fusion of the feature map is realized. Experiments compare the entropy, standard deviation, mutual information, edge retention, wavelet feature mutual information, visual information fidelity, and fusion efficiency of fused images under different fusion frameworks. The method in this paper is superior to the comparison algorithm in most indicators, and the target details are obvious and the outline are clear in the fused images.