Infrared and visible image fusion algorithm based on autoencoder
To address the current problems of inadequate image feature extraction,loss of information in the middle layer and insufficient details of fused images in the process of infrared and visible image fusion,this paper proposes an end-to-end image fusion network structure based on a self-encoder,which consists of three parts:encoder,fusion network and decoder.Firstly,the efficient channel attention mechanism and hybrid attention mechanism are introduced into the encoder and fusion network.The CRN(convolutional residual network)base blocks are used to extract and fuse the basic features of infrared images and visible images.The fused feature images are input to the decoder to reconstruct the fused images.Five representative methods are selected to compare with subjective and objective aspects.In the objective aspect,compared with the second place,AG、SF and VIF have increased by 21%,10.2%,and 7.2%.In the subjective aspect,significantly with clear targets,prominent details and obvious outline,which is in line with human visual perception.
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