Recognition of Fusion Processing Based on Infrared and Visible Images under the Framework of Semi-supervised Learning

Abstract In traditional convolutional networks, due to the lack of adequate information protection of traditional image fusion technology and the incomplete removal of redundant noise, the useful information of the fusion image is missing and the recognition success rate is low. In this paper, through the research of deep learning-based image fusion methods and traditional target recognition and SVM neural network, an image fusion processing recognition method based on infrared and visible light is designed. The coding network of this image fusion method consists of convolutional layers, fusion layers and dense blocks. The output of each layer needs to be connected to the next few layers by using a densely connected neural network, so as to obtain more useful features from the source image and fuse the data of the two images better. It is verified by simulation that the fused image has sound visual effects, and its edges and details have been completely preserved. Thus, the target object has strong recognizability compared with the surrounding environment. Research shows that this method will help to more accurately interpret target information in complex environments and achieve more effective results in target recognition.