Infrared and Visible Light Image Fusion Method Based on Improved Fully Convolutional Neural Network

Abstract

Image fusion technology based on deep learning is easy to lose the shallow feature information of the network and difficult to recognize the image accurately.For this reason,this paper proposes an infrared and visible light image fusion method that uses improved Fully Convolutional Neural Network(FCN).The Non-Subsampled Shearlet Transform(NSST) is used to decompose the source image in a multi-scale and multi-directional way to generate high-frequency and low-frequency sub-band images.Then the high-frequency sub-band is input into the FCN model to extract multi-scale features,and the high-frequency sub-band feature mapping graph is generated.The maximum weighted average algorithm is used to complete the fusion of high-frequency sub-band.At the same time,the local energy and fusion strategy are used to fuse the low-frequency sub-band,and the final fusion image is obtained by implementing NSST inverse transform on the fused high frequency sub-band and low frequency sub-band.Experimental results show that compared with GFF,WLS,IFE and other methods,the fusion method provides better visual effects of fused images and evaluation results of indexes.