Abstract:

In order to solve the problems of inadequate feature extraction， easy to lose middle layer information and long training time during infrared and visible image fusion， this paper proposed an end-to-end lightweight image fusion network structure based on gradient transfer and autoencoder. The network structure is composed of encoder， fusion layer and decoder. First， a Bottleneck block is introduced into the encoder， which is a Bottleneck block， and it uses the convolution layer and a bottleneck block to extract features from input infrared and visible images to get depth feature graphs. Then， the gradient extraction operator is introduced into the fusion layer， and the obtained depth feature graphs are processed to obtain the corresponding gradient graphs. Then， each depth feature map and the corresponding gradient map are fused in the fusion layer through the fusion layer strategy， the loss function is redesigned， the fused feature map and gradient map are spliced and input to the decoder for decoding to reconstruct the fusion image. Finally， the current representative fusion methods were selected for comparison and verification. In the eight fusion quality evaluation indexes of SF， MI， VIF， Qabf， SCD， AG， EN and SD， the performance of the first seven indexes improved significantly. They are improved by 57.4%， 54.6%， 28.3%， 74.2%， 23.8%， 43.1% and 1.3% respectively， and the performance of the eighth index is similar. In addition， network model parameter analysis and time complexity comparison experiment were conducted. The algorithm parameter in this paper is 12352， and the algorithm time is 1.1246 seconds. Experimental results show that the proposed method can quickly generate the fusion image with clear target， clear outline， prominent texture and human visual perception.

Key words: computer application, machine vision, infrared image, visible image, image fusion, gradient transfer, auto-encoder