Hyperspectral image (HSI) fusion can effectively improve the spatial resolution of HSIs by integrating high-resolution multispectral images (MSIs). Considering the spatial and spectral degradation relationship between a fused image and input images, a physics-based GAN is proposed to fuse HSI and MSI. A physical model estimating degradation of image is introduced in the generator and in the discriminators. For the generator, a set of recursive modules including a physical degradation model and a multiscale residual channel attention fusion module integrate the spectral-spatial difference information between input images and estimated degradation images to restore the details of the fused image. Subsequently, the residual spatial attention fusion module is used to combine the results of all recursions to obtain the final reconstructed result. As for the discriminators, three networks with the final fused image, estimated LR HSI and estimated MSI as inputs share the same architecture. Finally, the loss function that contains adversarial losses and L1 losses of the fused image and estimated degradation images is used to optimize network parameters. The experimental results demonstrate that the proposed method outperforms state-of-the-art methods.

中文翻译：

---

具有用于高光谱和多光谱图像融合的迭代细化单元的基于物理的 GAN

高光谱图像 (HSI) 融合可以通过集成高分辨率多光谱图像 (MSI) 来有效提高 HSI 的空间分辨率。考虑到融合图像和输入图像之间的空间和光谱退化关系，提出了一种基于物理的 GAN 来融合 HSI 和 MSI。在生成器和鉴别器中引入了一个估计图像退化的物理模型。对于生成器，一组递归模块包括物理退化模型和多尺度残差通道注意力融合模块，整合输入图像和估计退化图像之间的谱空间差异信息，以恢复融合图像的细节。随后，使用残差空间注意力融合模块将所有递归的结果组合起来，得到最终的重构结果。至于鉴别器，以最终融合图像、估计的 LR HSI 和估计的 MSI 作为输入的三个网络共享相同的架构。最后，包含融合图像和估计退化图像的对抗性损失和 L1 损失的损失函数用于优化网络参数。实验结果表明，所提出的方法优于最先进的方法。