Abstract

In the current image fusion techniques, typically dual-band images are fused to obtain a fused image with salient target information, or intensity and polarization images are fused to achieve an image with enhanced visual perception. However, the current lack of dual-band polarization image datasets and effective fusion methods pose significant challenges for extracting more information in a single image. To address these problems, we construct a dataset containing intensity and polarization images in the visible and near-infrared bands. Furthermore, we propose an end-to-end image fusion network using attention mechanisms and atrous spatial pyramid pooling to extract key information and multi-scale global contextual information. Moreover, we design efficient loss functions to train the network. The experiments verify that the proposed method achieves better performance than the state-of-the-art in both subjective and objective evaluations.

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