Image fusion aims to combine information from multiple source images into a single and more informative image. A major challenge for deep learning-based image fusion algorithms is the absence of a definitive ground truth and distance measurement. Thus, the manually specified loss functions aiming to steer the model learning, include hyperparameters that need to be manually thereby limiting the model's flexibility and generalizability to unseen tasks. To overcome the limitations of designing loss functions for specific fusion tasks, we propose a unified meta-learning based fusion framework named ReFusion, which learns optimal fusion loss from reconstructing source images. ReFusion consists of a fusion module, a loss proposal module, and a reconstruction module. Compared with the conventional methods with fixed loss functions, ReFusion employs a parameterized loss function, which is dynamically adapted by the loss proposal module based on the specific fusion scene and task. To ensure that the fusion network preserves maximal information from the source images, makes it possible to reconstruct the original images from the fusion image, a meta-learning strategy is used to make the reconstruction loss continually refine the parameters of the loss proposal module. Adaptive updating is achieved by alternating between inter update, outer update, and fusion update, where the training of the three components facilitates each other. Extensive experiments affirm that our method can successfully adapt to diverse fusion tasks, including infrared-visible, multi-focus, multi-exposure, and medical image fusion problems. The code will be released.

翻译：图像融合旨在将多幅源图像的信息融合为单一且信息更丰富的图像。基于深度学习的图像融合算法面临的主要挑战是缺乏确定的真实标签与距离度量。因此，人工设定的损失函数在引导模型学习时需手动调整超参数，限制了模型对未知任务的灵活性与泛化能力。为突破特定融合任务中损失函数设计的局限，我们提出统一的无监督融合框架ReFusion，该框架通过重构源图像学习最优融合损失。ReFusion由融合模块、损失提议模块和重构模块三部分组成。相较于采用固定损失函数的传统方法，ReFusion采用参数化损失函数，由损失提议模块根据具体融合场景与任务动态调整。为确保融合网络保留源图像的最大信息量，使其能够从融合图像中重构出原始图像，我们采用元学习策略，使重构损失持续优化损失提议模块的参数。通过交替执行内部更新、外部更新与融合更新实现自适应优化，三个模块的训练相互促进。大量实验证明，该方法可成功适配红外-可见光、多聚焦、多曝光及医学图像等多样化融合任务。代码将开源。