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The end-to-end image fusion framework has achieved promising performance,with dedicated convolutional networks aggregating the multi-modal localappearance. However, long-range dependencies are directly neglected in existingCNN fusion approaches, impeding balancing the entire image-level perception forcomplex scenario fusion. In this paper, therefore, we propose an infrared andvisible image fusion algorithm based on a lightweight transformer module andadversarial learning. Inspired by the global interaction power, we use thetransformer technique to learn the effective global fusion relations. Inparticular, shallow features extracted by CNN are interacted in the proposedtransformer fusion module to refine the fusion relationship within the spatialscope and across channels simultaneously. Besides, adversarial learning isdesigned in the training process to improve the output discrimination viaimposing competitive consistency from the inputs, reflecting the specificcharacteristics in infrared and visible images. The experimental performancedemonstrates the effectiveness of the proposed modules, with superiorimprovement against the state-of-the-art, generalising a novel paradigm viatransformer and adversarial learning in the fusion task.

TGFuse: An Infrared and Visible Image Fusion Approach Based on Transformer and Generative Adversarial Network