Infrared and visible image fusion algorithm for substation equipment based on NSCT and Siamese network

In order to accurately obtain the status information of substation equipment, a large number of infrared and visible images will be used in the process of equipment maintenance. Traditional image fusion methods often lose the temperature information of the image, resulting in low brightness and contrast in the fusion image; while deep learning fusion algorithm will lose some details. Therefore, this paper proposes an infrared and visible light fusion algorithm based on NSCT and Siamese network to improve the quality of fusion image. Firstly, the infrared and visible images are decomposed by NSCT; the high-frequency part and low-frequency part are fused by the fusion rule of guided filtering, and the new high-frequency subband coefficient FH and the new low-frequency subband FL are obtained; then the first fusion image is obtained by NSCT reconstruction of FH and FL; after that, the weight mapping image of the first fusion image and the infrared image is obtained by convolution network, and at the same time Laplacian pyramid is used to decompose the primary fusion image and infrared image, and Gaussian pyramid is used to decompose the weight map; finally, the primary fusion image subband, infrared image subband and weight map image subband are fused according to the local window energy fusion method, and the final image is reconstructed by Laplacian pyramid. Experiments show that the subjective and objective indicators of the fusion picture are all improved.