TGFuse: An Infrared and Visible Image Fusion Approach Based on Transformer and Generative Adversarial Network
This work addresses image fusion for complex scenarios in computer vision, offering a novel paradigm that combines transformer and adversarial learning, though it is incremental as it builds on existing fusion frameworks.
The paper tackled the problem of infrared and visible image fusion by addressing the neglect of long-range dependencies in existing CNN approaches, resulting in superior performance against state-of-the-art methods with concrete improvements demonstrated experimentally.
The end-to-end image fusion framework has achieved promising performance, with dedicated convolutional networks aggregating the multi-modal local appearance. However, long-range dependencies are directly neglected in existing CNN fusion approaches, impeding balancing the entire image-level perception for complex scenario fusion. In this paper, therefore, we propose an infrared and visible image fusion algorithm based on a lightweight transformer module and adversarial learning. Inspired by the global interaction power, we use the transformer technique to learn the effective global fusion relations. In particular, shallow features extracted by CNN are interacted in the proposed transformer fusion module to refine the fusion relationship within the spatial scope and across channels simultaneously. Besides, adversarial learning is designed in the training process to improve the output discrimination via imposing competitive consistency from the inputs, reflecting the specific characteristics in infrared and visible images. The experimental performance demonstrates the effectiveness of the proposed modules, with superior improvement against the state-of-the-art, generalising a novel paradigm via transformer and adversarial learning in the fusion task.