Infrared and Visible Image Fusion via Interactive Compensatory Attention Adversarial Learning

The existing generative adversarial fusion methods generally concatenate source images or deep features, and extract local features through convolutional operations without considering their global characteristics, which tends to produce a limited fusion performance. Toward this end, we propose a novel interactive compensatory attention fusion network, termed ICAFusion. In particular, in the generator, we construct a multi-level encoder-decoder network with a triple path, and design infrared and visible paths to provide additional intensity and gradient information for the concatenating path. Moreover, we develop the interactive and compensatory attention modules to communicate their pathwise information, and model their long-range dependencies through a cascading channel-spatial model. The generated attention maps can more focus on infrared target perception and visible detail characterization, and are used to reconstruct the fusion image. Therefore, the generator takes full advantage of local and global features to further increase the representation ability of feature extraction and feature reconstruction. Extensive experiments illustrate that our ICAFusion obtains superior fusion performance and better generalization ability, which precedes other advanced methods in the subjective visual description and objective metric evaluation.