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Deep learning-based image fusion approaches have obtained wide attention inrecent years, achieving promising performance in terms of visual perception.However, the fusion module in the current deep learning-based methods suffersfrom two limitations, \textit{i.e.}, manually designed fusion function, andinput-independent network learning. In this paper, we propose an unsupervisedadaptive image fusion method to address the above issues. We propose a featuremutual mapping fusion module and dual-branch multi-scale autoencoder. Morespecifically, we construct a global map to measure the connections of pixelsbetween the input source images. % The found mapping relationship guides theimage fusion. Besides, we design a dual-branch multi-scale network throughsampling transformation to extract discriminative image features. We furtherenrich feature representations of different scales through feature aggregationin the decoding process. Finally, we propose a modified loss function to trainthe network with efficient convergence property. Through sufficient training oninfrared and visible image data sets, our method also shows excellentgeneralized performance in multi-focus and medical image fusion. Our methodachieves superior performance in both visual perception and objectiveevaluation. Experiments prove that the performance of our proposed method on avariety of image fusion tasks surpasses other state-of-the-art methods, provingthe effectiveness and versatility of our approach.

Unsupervised Image Fusion Method based on Feature Mutual Mapping