Infrared and Visible Images Fusion Algorithm Based on NSST and IFCNN

Aiming at the problem of insufficient edge detail preservation in image fusion, infrared and visible images fusion algorithm based on non-subsampled shear-wave transform (NSST) and convolutional neural network image fusion framework (IFCNN) is proposed. First, infrared and visible images are decomposed by NSST. Then, in order to make the low frequency sub band image better highlight the contour information, the image is fused using similarity matching fusion rule ; for the high-frequency sub band images, the feature layers are extracted using IFCNN, and the maximum weight image of feature layer can be obtained through L-2 regularization, convolution operation, and maximum selection strategy processing, and the high frequency fusion rule can be determined according to the maximum weight image. Finally, the NSST inverse transform is used to obtain the final fusion image. The experimental results show that the proposed algorithm retains the details of image edges and textures, reduces artifacts and noises, and has good visual effects.