Abstract: At present,in the fusion of infrared and visible image,dataset lacks the real fusion image to guide the important diffe-rence information of the two modes required for the final fusion image.Most of the existing fusion methods only consider the tradeoff and interaction of the source image,ignoring the role of the fusion image in the fusion process.The important information in the fusion image can constrain the difference information of the source image.Therefore,this paper proposesCRN to better guide the extraction of important information in the source image required by the fused image.At the same time,improving the quality of fusion image reconstruction can further strengthen the guidance of important feature information of source image.The quality of the reconstructed image is related to the extracted features.Among existing feature extraction methods,CNN has poor performance in capturing global features,while Transformer has high computational complexity and poor learning ability of local features.On this basis,a CNN module D2 Block combined with MLP is introduced,which can effectively extract and fuse local features and remote dependencies by rolling feature mappings in different directions.A large number of qualitative and quantitative experiments on several public data sets show that the proposed method achieves better results than other advanced methods.

Key words: Image fusion, Contrastive representation learning, Feature extraction, Deep learning, Autoencoder