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Infrared images and visible images have different imaging principles and contain different information. The fusion of infrared and visible images can synthesize the information of both, and at the same time, the complete edge structure of infrared images can guarantee the acquisition of image information under harsh and complex environments. Therefore, this paper proposes an infrared and visible image fusion method based on deep learning. Visible and infrared image pairs are divided into high-frequency and low-frequency parts in this paper. The weighted average strategy is directly used to add the low-frequency parts of the fused image. This method Uses the ResNet network to visible and infrared images of the high frequency parts of image feature extraction. FISHER discriminant method was used to screen the extracted features, and ZCA whitening was performed on the selected features to further remove the redundant information in the features. The initial weight graph was obtained by L1 generalization of the whitening features, and the final weight graph was obtained by softmax method. The high-frequency parts of infrared image and visible light image were added according to the weights to get the fused image high-frequency part, and the high and low frequency parts of the fused image were added to get the final fused image. The experimental results were compared with other methods in terms of subjective feeling and objective indicators respectively. The experimental results showed that the proposed method was more natural in fusion effect and had advantages in objective indicators.
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