The deep-learning-based image restoration and fusion methods have achieved remarkable results. However, the existing restoration and fusion methods paid little research attention to the robustness problem caused by dynamic degradation. In this paper, we propose a novel dynamic image restoration and fusion neural network, termed as DDRF-Net, which is capable of solving two problems, i.e., static restoration and fusion, dynamic degradation. In order to solve the static fusion problem of existing methods, dynamic convolution is introduced to learn dynamic restoration and fusion weights. In addition, a dynamic degradation kernel is proposed to improve the robustness of image restoration and fusion. Our network framework can effectively combine image degradation with image fusion tasks, provide more detailed information for image fusion tasks through image restoration loss, and optimize image restoration tasks through image fusion loss. Therefore, the stumbling blocks of deep learning in image fusion, e.g., static fusion weight and specifically designed network architecture, are greatly mitigated. Extensive experiments show that our method is more superior compared with the state-of-the-art methods.

翻译：以深层学习为基础的图像恢复和聚合方法取得了显著成果,然而,现有的恢复和聚合方法对动态退化造成的稳健性问题几乎没有引起研究关注。在本文件中,我们提议建立一个新的动态图像恢复和聚合神经网络,称为DDRF-Net,它能够解决两个问题,即静态恢复和聚合,动态降解。为了解决现有方法的静态融合问题,引入了动态融合,以学习动态恢复和聚合重量。此外,还提议了动态降解内核,以提高图像恢复和融合的稳健性。我们的网络框架可以有效地将图像退化与图像融合任务结合起来,通过图像恢复损失为图像融合任务提供更详细的信息,并通过图像融合损失优化图像恢复任务。因此,在图像融合中深层学习的绊脚石,例如静态聚变重量和专门设计的网络结构,大大减轻了。广泛的实验表明,我们的方法与最先进的方法相比更为优越。