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.
In order to identify the camouflage materials in military targets, this paper extracts multiple features to study the difference in optical characteristics between natural targets and man-made camouflage materials. Since Fresnel reflection can be regarded as a statistical description of scattering, this paper uses a multi-angle polarization measurement device to measure polarization and scattering characteristics. According to the physical meaning of the Mueller-Jones matrix, the expressions of amplitude ratio and phase retardation are extracted. Based on Pauli decomposition, new scattering similarity parameter formulas is defined. We discuss the curves of three characteristic parameters and analyze the difference between natural objects and camouflage materials. The experimental results show that the characteristic curves change significantly at Brewster’s angle, which clearly distinguishes the target from the camouflage material.