Generative adversarial network (GAN) has recently demonstrated a powerful tool for infrared and visible image fusion. However, existing methods extract the features incompletely, miss some textures, and lack the stability of training. To cope with these issues, this article proposes a novel image fusion Laplacian pyramid GAN (IF-LapGAN). Firstly, a generator is constructed which consists of shallow features extraction module, Laplacian pyramid module, and reconstruction module. Specifically, the Laplacian pyramid module is a pyramid-style encoder-decoder architecture, which progressively extracts the multi-scale features. Moreover, the attention module is equipped in the decoder to effectively decode the salient features. Then, two discriminators are adopted to discriminate the fused image and two different modalities respectively. To improve the stability of adversarial learning, we propose to develop another side supervised loss based on the side pre-trained fusion network. Extensive experiments show that IF-LapGAN achieves 3.27%, 27.28%, 6.32%, 1.39%, 3.14%, 1.15% and 1.07% improvement gains in terms of $Q_{NMI}$ , $Q_{M}$ , $Q_{Yang}$ , $Q^{AB/F}$ , MI, VIF, and FMI, respectively, compared with the second best values.

中文翻译：

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用于红外和可见图像融合的拉普拉斯金字塔生成对抗网络

生成对抗网络 (GAN) 最近展示了一种强大的红外和可见图像融合工具。然而，现有方法提取的特征不完全，遗漏了一些纹理，缺乏训练的稳定性。为了应对这些问题，本文提出了一种新颖的图像融合拉普拉斯金字塔GAN（IF-LapGAN）。首先，构建一个由浅层特征提取模块、拉普拉斯金字塔模块和重建模块组成的生成器。具体来说，拉普拉斯金字塔模块是一种金字塔式的编码器-解码器架构，它逐步提取多尺度特征。此外，在解码器中配备了注意力模块，以有效地解码显着特征。然后，采用两个鉴别器分别鉴别融合图像和两种不同的模态。为了提高对抗学习的稳定性，我们建议在侧预训练融合网络的基础上开发另一种侧监督损失。大量实验表明，IF-LapGAN 在以下方面实现了 3.27%、27.28%、6.32%、1.39%、3.14%、1.15% 和 1.07% 的改进增益$Q_{NMI}$ ,$Q_{M}$ ,$Q_{杨}$ ,$Q^{AB/F}$ 、MI、VIF 和 FMI 分别与次优值进行比较。